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@inproceedings{bib_Sens_2025, AUTHOR = {Bhatt, Munjaal Tarunkumar and Hitesh, Maniar Aakash and Mulgundkar, Aditya Srinivas and Kandath, Harikumar and Gangadharan, Deepak }, TITLE = {Sensor Based Active Fault Tolerant Controller for Hexacopters}, BOOKTITLE = {International Conference on Robotics and Automation}. YEAR = {2025}}

In this work, we conduct performance analysis of a Fault Tolerant Control (FTC) scheme for hexacopter actuator failure through hardware-based fault detection and dynamic control reallocation. Hall effect sensors are used to monitor motor speeds and compared with the reference signals generated by the flight controller (FC) for anomaly detection. Experimental flight tests demonstrate the efficacy of the proposed FTC in detecting the faults and subsequent flight stabilization. The performance of the proposed FTC (quantified in terms of tracking error in attitude dynamics) is compared with the ideal fault detection (no delay between fault occurrence and control reallocation) case.

@inproceedings{bib_Imit_2025, AUTHOR = {Tripathi, Anubhav and Kandath, Harikumar and Govindan, Nagamanikandan }, TITLE = {Imitation Learning-based Control of Brachiation Motion with Anthropomorphic Hands}, BOOKTITLE = {Internationcal Conference on Control, Automation and Systems}. YEAR = {2025}}

Brachiation, inspired by ape locomotion, involves swinging from one substrate to another. Existing approaches typically rely on simple grippers and computationally expensive optimal control to compute feasible states and control trajectories. In contrast, learning-based methods often lack physical modeling and require extensive training data. We present a brachiating system using high degree-of-freedom anthropomorphic hands to generate swing trajectories and perform stable grasps in a physics-based simulation environment (MuJoCo). An optimal open-loop trajectory is first generated via trajectory optimization based on a desired grasp location. A tracking controller follows this reference, while a grasping controller activates upon proximity to ensure secure contact. To reduce computational cost, we train a Generative Adversarial Imitation Learning (GAIL) policy using expert trajectories from the optimization framework. The GAIL-based controller generalizes to perturbed conditions and eliminates the need for repeated re-optimization, significantly lowering computation time. It also adapts to varying initial configurations, removing the requirement to rerun optimization for each case. We compare the learned model with a traditional optimal controller and demonstrate marked improvements in both computational efficiency and versatility.

@inproceedings{bib_Meta_2024, AUTHOR = {Senthilnath, J and Kandath, Harikumar and Sundaram, Suresh }, TITLE = {Metacognitive Decision-Making Framework for Multi-UAV Target Search Without Communication}, BOOKTITLE = {IEEE Transactions on Systems, Man, and Cybernetics :Systems}. YEAR = {2024}}

This article presents a metacognitive decision-making (MDM) framework inspired by human-like metacognitive principles. The MDM framework is incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized stochastic search without communication for detecting and confirming stationary targets (fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple sensors (varying sensing capability) and search for targets in a largely unknown area. The MDM framework consists of a metacognitive component and a self-cognitive component. The metacognitive component helps to self-regulate the search with multiple sensors addressing the issues of “which-sensor-to-use,” “when-to-switch-sensor,” and “how-to-search.” Based on the information gathered by sensors carried by each UAV, the self-cognitive component regulates different levels of stochastic search and switching levels for effective searching, where the lower levels of search aim to localize a target (detection) and the highest level of a search exploit a target (confirmation). The performance of the MDM framework with two sensors having a low accuracy for detection and increased accuracy to confirm targets is evaluated through Monte Carlo simulations and compared with six decentralized multi-UAV search algorithms (three self-cognitive searches and three self and social-cognitive-based searches). The results indicate that the MDM framework can efficiently detect and confirm targets in an unknown environment.

@inproceedings{bib_An_E_2024, AUTHOR = {John, Josy and Kandath, Harikumar and Senthilnath, J and Sundaram, Suresh }, TITLE = {An Efficient Approach With Dynamic Multiswarm of UAVs for Forest Firefighting}, BOOKTITLE = {IEEE Transactions on Systems, Man, and Cybernetics :Systems}. YEAR = {2024}}

This article proposes the multiswarm cooperative information-driven search and divide and conquer mitigation control (MSCIDC) approach for faster detection and mitigation of forest fires by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of unmanned aerial vehicles (UAVs) flying together to search and quench the fire areas effectively. The multiswarm cooperative information-driven search uses a two-stage search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire locations. The search level is selected based on the thermal sensor information about the potential fire area. The dynamic nature of swarms acquired from global regulative repulsion and merging between swarms reduces the detection and mitigation time compared to the existing methods. The local attraction among the swarm members helps the nondetector members reach the fire location faster, and divide-and-conquer mitigation control ensures a nonoverlapping fire sector allocation for all members quenching the fire. The performance of the MSCIDC has been compared with different multi- UAV methods using a simulated pine forest environment. The Monte-Carlo simulation results indicate that the MSCIDC reduces the average forest area burnt by 65% and mission time by 60% compared to the best case of the multi- UAV approaches, guaranteeing a faster and more successful mission.

@inproceedings{bib_A_Tw_2024, AUTHOR = {Gupta, Nikita and Anand, Shikhar and Kumar, Deepak and Ramteke, Manojkumar and Kandath, Harikumar and Kodamana, Hariprasad }, TITLE = {A Twin Agent Reinforcement Learning Framework by Integrating Deterministic and Stochastic Policies}, BOOKTITLE = {Industrial & Engineering Chemistry Research}. YEAR = {2024}}

Developing a reinforcement learning (RL) framework that works satisfactorily in deterministic and stochastic environments is challenging. To address this problem, a twin agent RL framework is proposed in this work, wherein we amalgamate both stochastic and deterministic agents’ actions in a multiagent framework that works with a feedback mechanism that actively monitors the output. The proposed algorithm uses twin actor networks of different agents, corresponding to deterministic and stochastic agents, and an action selection critic network is used to choose the best action from both agents. Here, the algorithm blends the outcomes of two reinforcement learning (RL) agents, a stochastic agent and a deterministic agent, namely, Proximal Policy Optimization (PPO) and Twin Delayed Deep Deterministic Policy Gradient (TD3), respectively. We assess the effectiveness of the proposed algorithm by applying it to two case studies: (i) monoclonal antibody (mAb) production and (ii) production of propylene glycol (PG). The studies are conducted in the presence of parametric uncertainties, measurement noise, and nominal conditions. It is observed that for case study 1, the root-mean-square error (RMSE) value for the proposed algorithm is reduced by 40.9% when compared with TD3 and 27.57% when compared with PPO for the simulations. Similarly, for case study 2, the RMSE for the proposed algorithm is reduced by 8.87% when compared with TD3 and 5.8% with PPO. Based on extensive simulations, it is found that the proposed twin agent algorithm has faster convergence and better set-point tracking when compared to the agents operated individually.

@inproceedings{bib_The__2024, AUTHOR = {Gureja, Akshit and Hussain, Aftab M. and Viswanadh, Kandala Savitha and Walchatwar, Nagesh Laxman and Agrawal, Rishabh Anup and Sinha, Shiven and Chaudhari, Sachin and Vaidhyanathan, Karthik and Choppella, Venkatesh and Bhimalapuram, Prabhakar and Kandath, Harikumar }, TITLE = {The Engineering End-to-End Remote Labs using IoT-based Retrofitting}, BOOKTITLE = {IEEE Access}. YEAR = {2024}}

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.

@inproceedings{bib_Deep_2024, AUTHOR = {Kurande, Aditya and Joshi, Bhaskar and Kandath, Harikumar }, TITLE = {Deep RL Based Obstacle Avoidance for UAVs with Time Varying Sensor Bias}, BOOKTITLE = {IEEE SENSORS}. YEAR = {2024}}

Advancements in Deep Reinforcement Learning (DRL) have shown significant promise for the development of self directed Unmanned Aerial Vehicles (UAVs). Our research focuses on examining the impact of time-varying noise such as that present in Inertial Measurement Unit (IMU) sensors on the efficacy of DRL-based waypoint navigation and obstacle avoidance in UAVs. Sensor noise affects localization and obstacle detection, and is assumed to follow a Gaussian probability distribution with an unknown non-zero time varying mean and variance. In this study, we consider environment with static obstacles where noise exhibits a time-varying bias, a characteristic commonly associated with IMU sensors. We evaluate the effectiveness of a DRL agent, trained using the Proximal Policy Optimization (PPO) technique, in the presence of such noise.

@inproceedings{bib_Equi_2024, AUTHOR = {S, Vidya C and Kandath, Harikumar }, TITLE = {Equilibrium Point Selection and Two Stage Optimal Control of Quadrotor Under Actuator Failure}, BOOKTITLE = {International Conference on Control, Mechatronics and Automation}. YEAR = {2024}}

This paper presents a simple method for stabilizing the quadrotor dynamics under complete loss of one actuator using two-stage optimal control. Detailed equilibrium analysis and subsequent selection of operating point under actuator loss are provided, incorporating constraints on the maximum available thrust. A detailed simulation study using a high-fidelity nonlinear model of the quadrotor is presented showing the stability and performance of the closed-loop system under complete actuator loss, in the presence of external disturbances.

@inproceedings{bib_Crac_2024, AUTHOR = {Srivastava, Kushagra and Datta, Damodar and Tahereen, Rizvi and Ramancharla, Pradeep Kumar and Sarvadevabhatla, Ravi Kiran and Kandath, Harikumar }, TITLE = {CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures}, BOOKTITLE = {International conference on Pattern Recognition}. YEAR = {2024}}

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. The encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder's domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively. Code, models, and dataset will be made available.

@inproceedings{bib_Atte_2024, AUTHOR = {Sharma, Ashish and Suhas, Vaddhiparthy S V S L N Surya and Nagasri, Goparaju Sai Usha and Gangadharan, Deepak and Kandath, Harikumar }, TITLE = {Attention Meets UAVs: A Comprehensive Evaluation of DDoS Detection in Low-Cost UAVs}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2024}}

This paper explores the critical issue of enhancing cybersecurity measures for low-cost, Wi-Fi-based Unmanned Aerial Vehicles (UAVs) against Distributed Denial of Service (DDoS) attacks. In the current work, we have explored three variants of DDoS attacks, namely Transmission Control Proto col (TCP), Internet Control Message Protocol (ICMP), and TCP + ICMPflooding attacks, and developed a detection mechanism that runs on the companion computer of the UAV system. As a part of the detection mechanism, we have evaluated various machine learning, and deep learning algorithms, such as XGBoost, Isolation Forest, Long Short-Term Memory (LSTM), Bidirectional-LSTM (Bi-LSTM), LSTM with attention, Bi LSTM with attention, and Time Series Transformer (TST) in terms of various classification metrics. Our evaluation reveals that algorithms with attention mechanisms outperform their counterparts in general, and TST stands out as the most efficient model with a run time of ∼0.1 seconds. TST has demonstrated an F1 score of 0.999, 0.997, and 0.943 for TCP, ICMP, and TCP + ICMP flooding attacks respectively. In this work, we present the necessary steps required to build an on board DDoS detection mechanism. Further, we also present the ablation study to identify the best TST hyperparameters for DDoS detection, and we have also underscored the advantage of adapting learnable positional embeddings in TST for DDoS detection with an improvement in F1 score from 0.94 to 0.99.

@inproceedings{bib_Desi_2024, AUTHOR = {Gupta, Snehit and Rithwik, K P and Prasanth, Kurva and Ashe, Avijit Kumar and Kandath, Harikumar }, TITLE = {Design and Analysis of a Modular Flapping Wing Robot with a Swappable Powertrain Module}, BOOKTITLE = {IEEE International Conference on Mechatronics and Automation}. YEAR = {2024}}

Flapping-wing robots (FWR) have domain-specific applications, where the lack of a fast-rotating propeller makes them safer when operating in complex environments with human proximity. However, most existing research in flapping-wing robots focuses on improving range/endurance or increasing payload capacity. This paper proposes a modular powertrain-based flapping-wing robot as a versatile solution to a mission-specific priority switch between payload or range for the same FWR. As the flapping frequency and stroke amplitude directly influence the flight characteristics of the FWR, we exploit this relation when designing our swappable powertrain with different motorgearbox combinations and 4-bar crank lengths to obtain the desired frequency and amplitude. We calculate initial estimates for default configuration and simulate it using pterasoftware. We then fabricate two powertrain modules - a default configuration with a higher flapping frequency for payload purposes and an extended-range configuration with a tandem propeller for higher flight velocity for longer range and endurance. To verify the results, we compare the flight test data of both power train configurations using the same FWR platform.

@inproceedings{bib_RaCI_2024, AUTHOR = {Bansal, Harsh and Goyal, Vyom and Joshi, Bhaskar and Gupta, Akhil and Kandath, Harikumar }, TITLE = {RaCIL: Ray Tracing based Multi-UAV Obstacle Avoidance through Composite Imitation Learning}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2024}}

In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning ap- proach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the sig- nificant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments.

@inproceedings{bib_Visi_2024, AUTHOR = {Kumar, Jayakant and Himanshu, and Kandath, Harikumar and A, Pooja }, TITLE = {Vision Based Micro-UAV Navigation Through Narrow Passages}, BOOKTITLE = {International Conference on Control, Mechatronics and Automation}. YEAR = {2024}}

This research paper presents a novel low computational approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed, and then employs a tuned PID controller to guide the UAV through and out of the passage while avoiding collisions with the walls. To evaluate the effectiveness of the proposed approach, a series of experiments were conducted using a micro-UAV navigating in and out of a custom-built test environment (constrained rectangular box). The results demonstrate that the system is able to successfully guide the UAV through the passages while avoiding collisions with the walls

@inproceedings{bib_Leve_2024, AUTHOR = {Sehgal, Shivaan and Maniar, Aakash and K, Harikumar and Gangadharan, Deepak }, TITLE = {Leveraging Latent Temporal Features for Robust Fault Detection and Isolation in Hexacopter UAVs}, BOOKTITLE = {International Conference on Automation, Robotics and Applications}. YEAR = {2024}}

This paper introduces a novel approach for fault detection and localization in a motor of a Hexacopter UAV. The proposed two-stage architecture leverages Long Short-Term Memory (LSTM) networks for latent temporal feature extraction and Random Forest for localization. By combining them, we see improved fault detection and isolation performance. Our evaluations show the robustness of this approach in varying noise levels and real-world-like environments. Analysis of computational efficiency in rapid detection shows that the model identified faults within 2-5 time steps of the flight. Finally, we show that the proposed method surpasses classical statistical models and deep learning techniques in terms of overall accuracy (96.78%).

@inproceedings{bib_Acce_2023, AUTHOR = {S, Adithya and Kandath, Harikumar and J, Senthilnath }, TITLE = {Acceleration-Based PSO for Multi-UAV Source-Seeking}, BOOKTITLE = {Industrial Electronics Society}. YEAR = {2023}}

This paper presents a novel algorithm for a swarm of unmanned aerial vehicles to search for an unknown source. The proposed method is inspired by the well-known particle swarm optimization (PSO) algorithm and is called accelerationbased particle swarm optimization (APSO) to address the sourceseeking problem with no a priori information. Unlike the conventional particle swarm optimization algorithm, where the particle velocity is updated based on the self-cognition and socialcognition information, here the update is performed on the particle acceleration. A theoretical analysis is provided, showing the stability and convergence of the proposed acceleration-based particle swarm optimization algorithm. Conditions on the parameters of the resulting third-order update equations are obtained using Jury’s stability test. High-fidelity simulations performed in CoppeliaSim, show the improved performance of the proposed acceleration-based particle swarm optimization algorithm for searching an unknown source when compared with the state-ofthe-art particle swarm-based source-seeking algorithms. From the obtained results, it is observed that the proposed method performs better than the existing methods under scenarios like different inter unmanned aerial vehicle communication network topologies, varying numbers of unmanned aerial vehicles in the swarm, different sizes of search regions, restricted source movement, and in the presence of measurements noise.

@inproceedings{bib_Sim-_2023, AUTHOR = {Joshi, Bhaskar and Kapur, Dhruv and K, Harikumar }, TITLE = {Sim-to-Real Deep Reinforcement Learning based Obstacle Avoidance for UAVs under Measurement Uncertainty}, BOOKTITLE = {International Conference on Automation, Robotics and Applications}. YEAR = {2023}}

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based waypoint navigation and obstacle avoidance for UAVs. Measurement uncertainty originates from noise in the sensors used for localization and detecting obstacles. Measurement uncertainty/noise is considered to follow a Gaussian probability distribution with unknown non-zero mean and variance. We evaluate the performance of a DRL agent, trained using the Proximal Policy Optimization (PPO) algorithm in an environment with continuous state and action spaces. The environment is randomized with different numbers of obstacles for each simulation episode in the presence of varying degrees of noise, to capture the effects of realistic sensor measurements. Denoising techniques like the low pass filter and Kalman filter improve performance in the presence of unbiased noise. Moreover, we show that artificially injecting noise into the measurements during evaluation actually improves performance in certain scenarios. Extensive training and testing of the DRL agent under various UAV navigation scenarios are performed in the PyBullet physics simulator. To evaluate the practical validity of our method, we port the policy trained in simulation onto a real UAV without any further modifications and verify the results in a real-world environment.

@inproceedings{bib_Rein_2023, AUTHOR = {Shivani, Chepuri and K, Harikumar }, TITLE = {Reinforcement Learning-based Response Shaping Control of Dynamical Systems}, BOOKTITLE = {International Conference on Control, Mechatronics and Automation}. YEAR = {2023}}

The control system design specifications for dynamical systems are typically provided in terms of the desired transient response and steady-state response. Meeting such requirements for dynamical systems whose mathematical models are unavailable is a challenging task. In this paper, we propose a learning-based controller to achieve the desired control system design specifications for unknown dynamical systems. We consider a SoTA model-free reinforcement learning agent (TD3) in the continuous state and action space setting where the agent has no knowledge of system dynamics. The selection of an appropriate reward function is a key factor that shapes the response of the dynamical system when the learning-based controller is implemented. The resulting controller is trained and tested for first-order and second-order linear systems, as well as a nonlinear system. The resulting trajectories of the closed-loop systems indicate that the transient and steady-state response can be altered by choosing the appropriate reward function while adhering to the constraints imposed on the control input.

@inproceedings{bib_UAV-_2023, AUTHOR = {Srivastava, Kushagra and Patel, Dhruv and Jha, Aditya Kumar and Jha, Mohhit Kumar and Kiran, Santosh Ravi and Ramancharla, Pradeep Kumar and K, Harikumar and Krishna, K Madhava }, TITLE = {UAV-Based Visual Remote Sensing for Automated Building Inspection}, BOOKTITLE = {European Conference on Computer Vision Workshops}. YEAR = {2023}}

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building

@inproceedings{bib_Deve_2023, AUTHOR = {K, Harikumar and Pushpangathan, Jinraj V and Bera, Titas and Dhall, Sidhant and Bhat, M Seetharama }, TITLE = {Development and calibration of autopilot hardware for small fixed-wing air vehicles with flight test validation of linear output feedback controller}, BOOKTITLE = {International conference on Unmanned Aircraft Systems}. YEAR = {2023}}

This paper discusses the development of autopilot hardware for small fixed-wing air vehicles. Weight constraint is the critical factor in developing such hardware. The sensors and communication devices are selected based on the requirements and constraints of these air vehicles. The sensors used in the hardware are calibrated using a three-axis rotating platform. The software written in the autopilot hardware is flexible enough to incorporate complex estimation and control algorithms along with the hardware-in-loop simulations. Linear output feedback controllers are designed for fixed wing micro and nano air vehicles. Successful flight trials are conducted to demonstrate the utility of the autopilot hardware for small fixed-wing air vehicles

@inproceedings{bib_A_de_2023, AUTHOR = {Dutta, Rajdeep and K, Harikumar and Jayavelu, Senthilnath and Xiaoli, Li and Sundaram, Suresh and Pack, Daniel }, TITLE = {A decentralized learning strategy to restore connectivity during multi-agent formation control}, BOOKTITLE = {Neurocomputing}. YEAR = {2023}}

In this paper, we propose a decentralized learning algorithm to restore communication connectivity during multi-agent formation control. The time-varying connectivity profile of a mobile multi-agent system represents the dynamic information exchange capabilities among agents. While connected to the neighbors, each mobile agent in the proposed scheme learns to raise the team connectivity. When the inter-agent communication is lost, the associated trained neural network generates appropriate control actions to restore connectivity. The proposed learning technique leverages an adaptive control formalism, wherein a neural network tries to mimic the negative gradient of a value that relies on the agent-to-neighbor distances. All agents use the conventional consensus protocol during the connected multi-agent dynamics, and under communication loss, only the lost agent executes the neural network predicted actions to come back to the fleet. Simulation results demonstrate the effectiveness of our proposed approach for single/multiple agent loss even in the presence of velocity disturbances.

@inproceedings{bib_PASE_2023, AUTHOR = {K, Harikumar and Ferdaus, Md Meftahul and Ng, Zhen Wei and Zhou, Bangjian and Sundaram, Suresh and Li, Xiaoli and Jayavelu, Senthilnath }, TITLE = {PASE: An autonomous sequential framework for the state estimation of dynamical systems}, BOOKTITLE = {Expert Systems with Applications}. YEAR = {2023}}

Kalman filter (KF) and its variants are commonly used in estimating states of dynamical systems. For accurate estimation of states, researchers have developed intelligent KFs by combining them with machine learning algorithms. The inherent variations in the vehicle dynamics and uncertainties in the environment require the development of an autonomous sequential framework for continuous estimation. Further, the need for real-time estimation of states in certain applications requires low computer memory usage and low computational cost while implementing autonomous-structured learning algorithms. In this paper, a parsimonious autonomous sequential estimator (PASE) is proposed, which combines the KF-based estimator and autonomous-structured recurrent parsimonious learning machine (rPALM) in a sequential manner. The rPALM overcomes the dependency on target variables while point-to-hyperplane distance calculation. The performance of PASE has been evaluated extensively by comparing it with various batch-learning algorithms and single-pass learning-based intelligent estimators. The results clearly indicate that PASE provides better estimation accuracy with a compact architecture for both linear and nonlinear dynamical systems. Finally, the performance of PASE has been evaluated with experimental data for the state estimation of an unmanned ground vehicle while the training of the learning machine is performed with the simulated data. The estimation accuracy in such a scenario is justifying its appropriateness in real-

@inproceedings{bib_Mult_2023, AUTHOR = {Ashe, Avijit Kumar and Goli, Srikanth and K, Harikumar and Gangadharan, Deepak }, TITLE = {Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals}, BOOKTITLE = {International conference on Unmanned Aircraft Systems}. YEAR = {2023}}

Multivariate Data Analysis for Motor Failure Detection and Isolation in a Multicopter UAV using Real-Flight Attitude Signals

@inproceedings{bib_A_Co_2023, AUTHOR = {Suhas, Vaddhiparthy s V S L N Surya and Sreya, Garapati and Turlapati, Prudhvi Raj and Gangadharan, Deepak and K, Harikumar }, TITLE = {A Comprehensive Evaluation on the Impact of Various Spoofing Scenarios on GPS Sensors in a Low-Cost UAV}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2023}}

Unmanned Aerial Vehicles (UAVs), particularly low-cost UAVs, have become increasingly important due to their wide range of applications and ease of use. However, with the rapid growth of the UAV market, the rising security concerns pose a greater risk. One such primary concern is location spoofing attacks which can compromise UAV's navigation system, making it crucial to analyze various location-based attacks. In this paper, we identify 16 such GPS spoofing scenarios based on environmental conditions, attack type, and spoof signal propagation path. We evaluate these scenarios based on various GPS parameters like Horizontal Dilution Of Precision (HDOP), Vertical Dilution Of Precision (VDOP), GPS satellite count in view, and avg signal-to-noise power density (CN0). We then analyze the variations in GPS parameters for various such attack scenarios. Further, we analyze the impact of distance on average CN0 and the effect of satellite count on effective spoofable distance. We also discuss several critical insights which are empirically observed during our experimental trials. Our experiments revealed that the natural conditions within indoor and outdoor scenarios can vary considerably, and effective spoofable distance can be up to 100 meters when the satellite count is less than 10.

@inproceedings{bib_Faul_2023, AUTHOR = {Mulgundkar, Aditya Srinivas and Singh, Mayank and Bhatt, Munjaal Tarunkumar and Turlapati, Prudhvi Raj and Gangadharan, Deepak and K, Harikumar }, TITLE = {Fault Detection and Isolation on a Hexacopter UAV using a Two-stage classification method}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2023}}

This paper presents the analysis and results of a data-driven approach for fault detection and isolation in case of complete failure of a single motor on a hexacopter. The proposed approach consists of a two-stage architecture using the Rotation Forest algorithm, which can detect faults without any false alarms and achieve a true positive classification rate of 92.6% and a false positive classification rate of 0.06%. The classification results are compared to other methods such as Logistic Regression, Gaussian Naive Bayes, AdaBoost, and Random Forest. Over 120 datasets containing approximately 21,000 data points are generated in simulation - divided into two sets for training and validation of the model. Outdoor flight tests are performed to validate the classifier algorithm further. We can detect and classify the fault within 60ms of its occurrence. A dataset is published in the open-source domain and can be used for training similar models. The work presented in this paper is data-driven (or model free) since the classifier has no knowledge of the parameters of the UAV and is derived only based on the functional relationship between input and output variables.

@inproceedings{bib_Pred_2023, AUTHOR = {Mundheda, Vedant and Mirakhor, Karan and S, Rahul K and K, Harikumar and Govindan, Nagamanikandan }, TITLE = {Predictive Barrier Lyapunov Function Based Control for Safe Trajectory Tracking of an Aerial Manipulator}, BOOKTITLE = {European Control Conference}. YEAR = {2023}}

This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) for the safe operation of the AM. BLF-based constraints are proposed for two objectives, viz. 1) To avoid the AM from colliding with static obstacles like a rectangular wall, and 2) To maintain the end effector of the manipulator within the desired workspace. The proposed BLF ensures that the above-mentioned objectives are satisfied even in the presence of unknown bounded disturbances. The capabilities of the proposed controller are demonstrated through high-fidelity non-linear simulations with parameters derived from a real laboratory scale AM. We compare the performance of our controller with other state-of-the-art MPC controllers for AM.

@inproceedings{bib_Real_2023, AUTHOR = {Singh, Jaskirat and Adwani, Neel and K, Harikumar and Krishna, K Madhava }, TITLE = {Real-Time Heuristic Framework for Safe Landing of UAVs in Dynamic Scenarios}, BOOKTITLE = {International conference on Unmanned Aircraft Systems}. YEAR = {2023}}

The world we live in is full of technology and with each passing day the advancement and usage of UAVs increases efficiently. As a result of the many application scenarios, there are some missions where the UAVs are vulnerable to external disruptions, such as a ground station's loss of connectivity, security missions, safety concerns, and delivery-related missions. Therefore, depending on the scenario, this could affect the operations and result in the safe landing of UAVs. Hence, this paper presents a heuristic approach towards safe landing of multi-rotor UAVs in the dynamic environments. The aim of this approach is to detect safe potential landing zones - PLZ, and find out the best one to land in. The PLZ is initially, detected by processing an image through the canny edge algorithm, and then the diameter-area estimation is applied for each region with minimal edges. The spots that have a higher area than the vehicle's clearance are labeled as safe PLZ. Onto the second phase of this approach, the velocities of dynamic obstacles that are moving towards the PLZs are calculated and their time to reach the zones are taken into consideration. The ETA of the UAV is calculated and during the descending of UAV, the dynamic obstacle avoidance is executed. The approach tested on the real-world environments have shown better results from existing work.

@inproceedings{bib_TASA_2023, AUTHOR = {Joshi, Tanuja and Kodamana, Hariprasad and K, Harikumar and Kaisare, Niket }, TITLE = {TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control}, BOOKTITLE = {Control Engineering Practice}. YEAR = {2023}}

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, these problems become harder to address for advanced model-based control strategies. Reinforcement Learning (RL), wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor-critic architecture have recently become popular for controlling systems where state and action spaces are continuous. It has been shown that an ensemble of actor and critic networks further helps the agent learn better policies due to the enhanced exploration due to simultaneous policy learning. To this end, the current study proposes a stochastic actor-critic RL algorithm, termed Twin Actor Soft Actor-Critic (TASAC), by incorporating an ensemble of actors for learning, in a maximum entropy framework, for batch process control.

@inproceedings{bib_Deep_2022, AUTHOR = {Pushpangathan, Jinraj V and K, Harikumar and Francis, Bibin }, TITLE = {Deep Reinforcement Learning and Simultaneous Stabilization-Based Flight Controller for Nano Aerial Vehicle}, BOOKTITLE = {IFAC-PapersOnLine}. YEAR = {2022}}

The plants of nano aerial vehicles (NAVs) are inherently unstable. Hence, a NAV needs a flight controller to accomplish a mission. Furthermore, the sensing and computational capabilities of NAV's autopilot hardware are limited. Hence, the implementation of the full state feedback controllers with gain scheduling is difficult. This paper proposes a flight controller scheme that consists of two parts: a Simultaneously Stabilizing Output Feedback Linear (SSOFL) controller and a Proximal Policy Optimization (PPO) deep reinforcement learning agent, which is connected in parallel to the SSOFL controller. In this scheme, the single SSOFL controller provides stabilization and nominal tracking performance to the NAV throughout its flight envelope by accomplishing simultaneous stabilization (SS). Additionally, the PPO agent is trained using the closed-loop (CL) nonlinear plant with this SSOFL controller to enhance the tracking performance. The effectiveness of the proposed flight controller scheme is verified using the six-degree-of-freedom nonlinear simulations of the fixed-wing nano aerial vehicle.

@inproceedings{bib_Wayp_2022, AUTHOR = {Kumar, Himanshu and K, Harikumar and Pushpangathan, Jinraj V }, TITLE = {Waypoint Navigation of Quadrotor using Deep Reinforcement Learning}, BOOKTITLE = {IFAC-PapersOnLine}. YEAR = {2022}}

This paper proposes a Reinforcement Learning (RL) based technique to develop a simple neural network controller for the task of waypoint navigation in quadrotors. In this paper, the application of Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithm for high and low-level control implementation for quadrotors is discussed. The proposed methods are tested on high fidelity Gym-Pybullet-Drones simulator. The effectiveness of the methods developed is validated through numerical simulations. The simulation results indicate that both control policies are successful in navigating through the assigned waypoint, with the low-level controller being accurate in the nominal flight conditions. In the presence of disturbance inputs, the high-level controller performs better when compared to the low-level controller.

@inproceedings{bib_An_E_2022, AUTHOR = {John, Josy and K, Harikumar and Senthilnath, J. and Sundaram, Suresh }, TITLE = {An Efficient Approach with Dynamic Multi-Swarm of UAVs for Forest Firefighting}, BOOKTITLE = {Technical Report}. YEAR = {2022}}

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multiswarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by 65% and mission time by 60% compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission. Index Terms—Swarm search, forest firefighting, cooperative information-driven search, divide and conquer mitigation control

@inproceedings{bib_Auto_2022, AUTHOR = {Jana, Shuvrangshu and Shivkumar, Shashank and Shewale, Mayur and K, Harikumar and Ramesh, Meghana and Balasubramaniam, Susheel and Khanapuri, Eshaan and Bhat, M Seetharama }, TITLE = {Autonomous Flight Test of a Novel Nonconventional Biplane Micro Air Vehicle}, BOOKTITLE = {Journal of Aerospace Engineering}. YEAR = {2022}}

This paper presents detailed mathematical modeling, controller design, and flight test results for the autonomous mission of a nonconventional fixed-wing biplane micro air vehicle (MAV) called Skylark having span and chord length within 150 mm. Although numerous fixed-wing MAV designs are reported in the open literature, an MAV’s reliable autonomous flight is still a challenge. The primary difficulties in performing the autonomous mission of fixed-wing MAV are system integration within the weight and power budget, center of gravity (CG) management, and flight controller design for fast dynamics. In this paper, the key challenges are addressed by using the higher payload capacity of Skylark, suitable selection and design of avionics components, and design of controller after detailed development of the mathematical model, including the additional effect of propeller wash and motor countertorque. An …

@inproceedings{bib_Opti_2022, AUTHOR = {K, Harikumar and Dutta, Rajdeep and Senthilnath, J. }, TITLE = {Optimal Connectivity during Multi-agent Consensus Dynamics via Model Predictive Control}, BOOKTITLE = {American Control Conference}. YEAR = {2022}}

In this paper, we solve an optimal consensus control problem of maximizing the state-dependent communi- cation connectivity during a multi-agent consensus dynamics. A proportional-derivative type consensus controller is leveraged to drive agents into a symmetric formation. The asymptotic stability of the closed-loop system dynamics is established using Lyapunov theory, which helps us to deduce an intuitive time-varying gain profile based on a sufficient condition for convergence. Further, a Model Predictive Control approach is adopted to minimize a quadratic cost over a finite prediction horizon by adjusting the controller gains, such that the optimal connectivity is attained on the way with less control efforts, while handling constraints to agents’ states, inputs, turn-rates and disturbances injected into agent velocities. Simulation results with time-varying controller gains demonstrate the impact of our proposed technique

@inproceedings{bib_Desi_2022, AUTHOR = {Jana, Shuvrangshu and K, Harikumar and Shewale, Mayur and Dhingra, Gunjit and Harish, Duddela Sai and Bhat, M Seetharama }, TITLE = {Design and development of a novel fixed-wing biplane micro air vehicle with enhanced static stability}, BOOKTITLE = {CEAS Aeronautical Journal}. YEAR = {2022}}

A detailed design approach undertaken in the development of “Skylark” is presented in this paper. “Skylark” is a non-conventional fixed-wing biplane Micro Air Vehicle (MAV) with a wingspan and chord length within 150 mm. It is specially designed with the ability to host onboard vision-assisted autonomous navigation systems. Fixed-wing MAV with capabilities of vision assisted autonomous navigation is not reported in the open literature. To stay within the maximum dimensional constraint, flying wing configuration with a low aspect ratio is preferred for MAV design, and therefore, the stability is inadequate due to lower static margin when compared to bigger Unmanned Aerial Vehicles (UAVs). In this paper, the novel design strategy addresses the major challenges such as high payload-carrying capacity, stability, and onboard processing required for vision-assisted autonomous navigation. The higher payload …

@inproceedings{bib_UAV-_2022, AUTHOR = {Srivastava, Kushagra and Srivastava, Kushagra and Jha, Aditya Kumar and Jha, Mohhit Kumar and Singh, Jaskirat and Kiran, Santosh Ravi and Ramancharla, Pradeep Kumar and K, Harikumar and Krishna, K Madhava }, TITLE = {UAV-based Visual Remote Sensing for Automated Building Inspection}, BOOKTITLE = {European Conference on Computer Vision Workshops}. YEAR = {2022}}

Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component’s contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters. The key parameters considered here are the distances between adjacent buildings, building plan-shape, building plan area, objects on the rooftop and rooftop layout. The accuracy of the proposed methodology in estimating the above-mentioned parameters is verified through field measurements taken using a distance measuring sensor and also from the data obtained through Google Earth. Additional details and code can be accessed from h

@inproceedings{bib_Pred_2022, AUTHOR = {Khare, Ishaan and K, Harikumar and Krishna, K Madhava }, TITLE = {Predictive optimal collision avoidance for a swarm of fixed-wing aircraft in 3D space}, BOOKTITLE = {International conference on Unmanned Aircraft Systems}. YEAR = {2022}}

In this paper, we propose a predictive and cooperative optimal control based autonomous navigation and collision avoidance for multiple fixed-wing aircraft (FWA) that also takes into account the physical constraints of the aircraft. The proposed method is implemented in a cooperative framework, analogous to the Reciprocal Velocity Obstacle (RVO) framework for autonomous navigation and collision avoidance for FWA swarm moving in a three dimensional (3D) space. Also, the change in performance with respect to varying prediction horizon is analyzed through numerical simulations. As a result of predictive optimal control and cooperation, we get less conservative maneuvers, reduced path lengths, and less deviation from the shortest path when compared to the conventional method without prediction and cooperation. Simulation results are provided, highlighting the advantages of the proposed method when compared to a popular method (Optimal reciprocal collision avoidance) for collision avoidance in 3D for FWA swarm and also against FGA algorithm (Fast geometric avoidance algorithm).

@inproceedings{bib_Robu_2022, AUTHOR = {Pushpangathan, Jinraj V and K, Harikumar and Dutta, Rajdeep and Bardhan, Rajarshi and Senthilnath, J }, TITLE = {Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and Inclusion of Agents and Switching Topologies}, BOOKTITLE = {Technical Report}. YEAR = {2022}}

Some of the issues associated with the prac- tical applications of consensus of multi-agent systems (MAS) include switching topologies, attrition and inclusion of agents from an existing network, and model uncertain- ties of agents. In this paper, a single distributed dynamic state-feedback protocol referred to as the Robust Attrition- Inclusion Distributed Dynamic (RAIDD) consensus proto- col, is synthesized for achieving the consensus of MAS with attrition and inclusion of linear time-invariant higher-order uncertain homogeneous agents and switching topologies. A state consensus problem termed as the Robust Attrition- Inclusion (RAI) consensus problem is formulated to find this RAIDD consensus protocol. To solve this RAI con- sensus problem, first, the sufficient condition for the exis- tence of the RAIDD protocol is obtained using the ν-gap metric-based simultaneous stabilization approach. Next, the RAIDD consensus protocol is attained using the Glover- McFarlane robust stabilization method if the sufficient con- dition is satisfied. The performance of this RAIDD protocol is validated by numerical simulations.

@inproceedings{bib_Robu_2022, AUTHOR = {Pushpangathan, Jinraj V. and K, Harikumar and Sundaram, Suresh and Sundararajan, Narasimhan }, TITLE = {Robust Simultaneously Stabilizing Decoupling Output Feedback Controllers for Unstable Adversely Coupled Nano Air Vehicles}, BOOKTITLE = {International Conference on Systems, Man, and Cybernetics}. YEAR = {2022}}

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input-multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum v-gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

@inproceedings{bib_Twin_2021, AUTHOR = {Joshi, Tanuja and Makker, Shikhar and Kodamana, Hariprasad and K, Harikumar }, TITLE = {Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control}, BOOKTITLE = {Computers & Chemical Engineering}. YEAR = {2021}}

Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from experiences. Recent studies in the literature have indicated the advantage of having an ensemble of actors in actor-critic Reinforcement Learning (RL) frameworks for improving the policy. The present study proposes an actor-critic RL algorithm, namely, twin actor twin delayed deep deterministic policy gradient (TATD3), by incorporating twin actor networks in the existing twin-delayed deep deterministic policy gradient (TD3) algorithm for the continuous control. In addition, two types of novel reward functions are also proposed for TATD3 controller. We showcase the efficacy of the TATD3 based controller for various batch process examples by comparing it with some of the existing RL algorithms presented in the literature.

@inproceedings{bib_Inco_2021, AUTHOR = {MALI, PRAVIN and K, Harikumar and Singh, Arun Kumar and Krishna, K Madhava and Sujit, P.B. }, TITLE = {Incorporating Prediction in Control Barrier Function Based Distributive Multi-Robot Collision Avoidance}, BOOKTITLE = {European Control Conference}. YEAR = {2021}}

Control barrier function (CBF) constraints provide a rigorous characterization of the space of control inputs that ensure the satisfaction of state constraints, such as collision avoidance, at all time instants. However, CBFs are highly nonlinear and non-convex and thus, when incorporated within an optimization-based algorithm such as Model Predictive Control (MPC), leads to a computationally challenging problem. Existing works by-pass the computational intractability by collapsing the horizon of the MPC to a single step, although this comes at the cost of severe degradation of performance. In this paper, we present two contributions to ensure the real-time performance of CBFs based MPC over long horizons in the context of multi-robot collision avoidance. First, we propose a customized Project Gradient Descent Method that incurs minimal computational overhead over existing one-step approaches but leads to a substantial improvement in trajectory smoothness, time to reach the goal, etc. Our second contribution lies in applying the proposed MPC to both quadrotors and fixed-wing aerial aircrafts (FWA). In particular, we show that the formulation for the quadrotors can be readily extended to the latter by deriving additional CBFs for the curvature and forward velocity constraints. We validate our algorithm with an extensive simulation of up to 10 robots in challenging benchmark scenarios.

@inproceedings{bib_Deep_2021, AUTHOR = {Kocer, Basaran Bahadir and Hady, Mohamad Abdul and K, Harikumar and Pratama, Mahardhika and Kovac, Mirko }, TITLE = {Deep Neuromorphic Controller with Dynamic Topology for Aerial Robots}, BOOKTITLE = {International Conference on Robotics and Automation}. YEAR = {2021}}

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

@inproceedings{bib_Mode_2021, AUTHOR = {SAHU, ANIMESH and K, Harikumar and Krishna, K Madhava }, TITLE = {Model predictive control based algorithm for multi-target tracking using a swarm of fixed wing UAVs}, BOOKTITLE = {International Conference on Automation Science and Engineering}. YEAR = {2021}}

— This paper presents a model predictive control (MPC) based algorithm for tracking multiple targets using a swarm of unmanned aerial vehicles (UAVs). All the UAVs belong to fixed-wing category with constraints on flight velocity, climb rate and turn rate. Each UAV carries a camera to detect and track the target. Two cases are considered where for the first case, the number of the UAVs is equal to the number of targets. For the second case, the number of UAVs is lesser than the number of targets leading to a conservative solution where the objective is to maximize the average time duration for which the targets are in the field-of-view (FOV) of any one of the UAV’s camera. A data driven Gaussian process (GP) based model is developed to relate the hyperparameters used in MPC to the mission efficiency. Bayesian optimization is performed to obtain the hyperparameters of the MPC that maximize the mission efficiency. Numerical simulations are performed for both cases using algorithm based on distributed MPC formulation. A performance comparison is provided with the centralized MPC formulation.

@inproceedings{bib_Appl_2021, AUTHOR = {Joshi, Tanuja and Makker, Shikhar and K, Harikumar }, TITLE = {Application of twin delayed deep deterministic policy gradient learning for the control of transesterification process}, BOOKTITLE = {Technical Report}. YEAR = {2021}}

The persistent depletion of fossil fuels has encouraged mankind to look for alternatives fuels that are renewable and environment-friendly. One of the promising and renewable alternatives to fossil fuels is bio-diesel produced by means of the batch transesterification process. Control of the batch transesterification process is difficult due to its complex and non-linear dynamics. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from the experiences. To achieve the same, this study explores the feasibility of reinforcement learning (RL) based control of the batch transesterification process. In particular, the present study exploits the application of twin delayed deep deterministic policy gradient (TD3) based RL for the continuous control of the batch transesterification process. These results showcase that TD3 based controller is able to control batch transesterification process and can be a promising direction towards the goal of artificial intelligence-based control in process industries.

@inproceedings{bib_BS-M_2021, AUTHOR = {Senthilnath, J. and Kumar, Abhishek and Jain, Anurag and K, Harikumar and Thapa, Meenakumari and , S. Suresh and Benediktsson, Jón Atli }, TITLE = {BS-McL: Bilevel Segmentation Framework With Metacognitive Learning for Detection of the Power Lines in UAV Imagery}, BOOKTITLE = {International Geoscience and Remote Sensing Symposium}. YEAR = {2021}}

In this article, we propose a bilevel segmentation framework with metacognitive learning (BS-McL) to detect power lines with an RGB camera mounted on an unmanned aerial vehicle (UAV) platform. The proposed framework consists of two levels based on spectral and spatial techniques. In the first level, spectral classification is carried out using the McL method, which is an evolving online learning neural network architecture. Due to similarities in spectral intensities, few nonpower line pixels are grouped along with power line pixels. The nonpower line pixels are removed by spatial segmentation in the second level. The second level includes morphological operations such as geometric features (shape and density indices), which are applied to detect the power lines. The processing steps of BS-McL are illustrated using a synthetic image of size 9 x 6 pixels. Also, two datasets consisting of 64 images with varying backgrounds, different locations, and dimensions of power lines are used to demonstrate the performance of the proposed BS-McL. The obtained results for BS-McL are compared with five commonly used methods. For both datasets, the efficiency of the BS-McL for power line extraction is better than for the methods used for comparison. Furthermore, the trained knowledge from our experimental set-up (Dataset 1: suburban scene) can be transferred to another dataset that is available publicly (Dataset 2: urban and mountain scenes) if the power line spectral values are in relevance with the distribution in the training dataset. The proposed approach BS-McL is based on online learning with a self-adaptive architecture, which provides improved generalization ability.

@inproceedings{bib_Hit-_2020, AUTHOR = {Pushpangathan, Jinraj V and K, Harikumar and Balakrishnan, Ajithkumar }, TITLE = {Hit-to-kill accurate minimum time continuous second-order sliding mode guidance for worst-case target maneuvers}, BOOKTITLE = {Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering}. YEAR = {2020}}

The recent research is focused on the development of an advanced interceptor missile that has hit-to-kill accuracy against ballistic targets performing evasive maneuvers. In this paper, a guidance law that achieves hit-to-kill accuracy against ballistic target executing worst-case maneuvers is developed using second-order sliding mode control and optimal control. The guidance law thus developed is continuous and has minimum time convergence for worst-case target maneuvers. The performance of the continuous guidance law with minimum time convergence is evaluated through numerical simulations against ballistic targets executing step maneuvers with changing polarity and weaving maneuvers.

@inproceedings{bib_Inte_2020, AUTHOR = {K, Harikumar and Pushpangathan, Jinraj V and Bhat, M Seetharama }, TITLE = {Integrated guidance and control framework for the waypoint navigation of a miniature aircraft with highly coupled longitudinal and lateral dynamics}, BOOKTITLE = {Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering}. YEAR = {2020}}

A solution to the waypoint navigation problem for the fixed wing micro air vehicles (MAV) having a severe coupling between longitudinal and lateral dynamics, in the framework of integrated guidance and control (IGC) is addressed in this paper. IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the coupled MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in IGC framework. A waypoint navigation algorithm is proposed that handles the minimum turn radius constraint of the MAV and also evaluates the feasibility of reaching a waypoint. Non-linear simulations with and without wind disturbances are performed on a high fidelity 150 mm wingspan MAV model to demonstrate the proposed waypoint navigation algorithm.

@inproceedings{bib_Effe_2020, AUTHOR = {K, Harikumar and Pushpangathan, Jinraj V and Sundaram, Suresh }, TITLE = {Effects of propeller flow on the longitudinal and lateral dynamics and model couplings of a fixed-wing micro air vehicle}, BOOKTITLE = {Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering}. YEAR = {2020}}

This paper analyzes the effects of propeller flow on the linear coupled longitudinal and lateral dynamics of a 150 mm wingspan fixed wing micro air vehicle (MAV). The effects propeller flow on the lift, drag, pitching moment and side force is obtained through wind tunnel tests. The aerodynamic forces and moments are modeled as a function of angle of attack, sideslip angle, control surface deflection and propeller rotation per minute. The nonlinear six degrees of freedom model is linearized about straight and constant altitude flight conditions for different trim airspeed to obtain linear coupled longitudinal and lateral state space model. The eigenvalues and eigenvectors of linear coupled longitudinal and lateral state space model are compared with and without propeller flow effects. The variation in the natural frequencies and damping ratios of short period mode, phugoid mode and Dutch roll mode are analyzed for various trim airspeed. An increase in the natural frequency is observed for phugoid mode and Dutch roll mode with propeller effects. The stability of the spiral mode is enhanced by the propeller flow and also the response of the roll subsidence mode is faster with propeller effects. Detailed analysis of eigenvalues and eigenvectors shows the importance of incorporating propeller flow in analyzing the dynamics of the MAV

@inproceedings{bib_Gap__2020, AUTHOR = {Pushpangathan, Jinraj V. and K, Harikumar and Sundaram, Suresh and Sundararajan, Narasimhan }, TITLE = {Gap Reduced Minimum Error Robust Simultaneous Estimation For Unstable Nano Air Vehicle}, BOOKTITLE = {Technical Report}. YEAR = {2020}}

This paper proposes a novel Gap Reduced Minimum Error Robust Simultaneous (GRMERS) estimator for resource-constrained Nano Aerial Vehicle (NAV) that enables a single estimator to provide simultaneous and robust estimation for a given N unstable and uncertain NAV plant models. The estimated full state feedback enables a stable flight for NAV. The GRMERS estimator is implemented utilizing a Minimum Error Robust Simultaneous (MERS) estimator and Gap Reducing (GR) compensators. The MERS estimator provides robust simultaneous estimation with minimal largest worst-case estimation error even in the presence of a bounded energy exogenous disturbance signal. The GR compensators reduce the gap between the graphs of N linear plant models to decrease the estimation error generated by the MERS estimator. A sufficient condition for the existence of a simultaneous estimator is established using LMIs and robust estimation theory. Further, MERS estimator and GR compensator design are formulated as non-convex tractable optimization problems and are solved using the population-based genetic algorithms. The performance of the GRMERS estimator consisting of MERS estimator and GR compensators from the population-based genetic algorithms is validated through simulation studies. The study results indicate that a single GRMERS estimator can produce state estimates with reduced errors for all flight conditions. The results indicate that the single GRMERS estimator is robust than the individually designed H inifinity filters.

@inproceedings{bib_Real_2019, AUTHOR = {Hady, Mohamad Abdul and Kocer, Basaran Bahadir and K, Harikumar and Pratama, Mahardhika }, TITLE = {Real-time UAV Complex Missions LeveragingSelf-Adaptive Controller with Elastic Structure}, BOOKTITLE = {Technical Report}. YEAR = {2019}}

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.

@inproceedings{bib_Mult_2007, AUTHOR = {Garg, Vishal and VIVEK, SHIRALKAR DIPTI and Rao, K.Prabhakara }, TITLE = {Multiple Film Based Daylight Control System}, BOOKTITLE = {REHVA World Congress, Antalya.}. YEAR = {2007}}

A multiple film based daylight control system for window has been developed to maintain the illuminance level at task plane. The developed system consists of three films with a visual transmittivity (Tvis) range of 0.159-0.015 and a 2x55W dimmable compact fluorescent lamp (CFL) fixture. The system works on the principle of feedback control, which records the changes in light intensity with the help of photosensor placed on the ceiling and facing the task plane and accordingly adjusts the film-position as well as the control voltage of dimmable CFL to maintain the task plane illuminance within a tolerance of-10%,+ 20% of setpoint. Optimized use of solar illuminance saves energy used for the task lighting for the period from 8: 00 to 19: 00, which was found to be about 70%. The paper describes the design, implementation of the proposed system and the preliminary results for task light illuminance and energy savings achieved in laboratory setup.