@inproceedings{bib_An_i_2026, AUTHOR = {Thomas, Antony and Mastrogiovanni, Fulvio and Baglietto, Marco }, TITLE = {An incremental sampling and segmentation-based approach for motion planning infeasibility}, BOOKTITLE = {Robotics and Autonomous systems}. YEAR = {2026}}

We present a simple and easy-to-implement algorithm to detect plan infeasibility in kinematic motion planning. Our method involves approximating the robot’s configuration space to a discrete space, where each degree of freedom has a finite set of values. The obstacle region separates the free configuration space into different connected regions. For a path to exist between the start and goal configurations, they must lie in the same connected region of the free space. Thus, to ascertain plan infeasibility, we merely need to sample adequate points from the obstacle region that isolate start and goal. Accordingly, we progressively construct the configuration space (initially assumed to be entirely free) by sampling from the discretized space and updating the bitmap cells representing obstacle regions. Subsequently, we partition this partially built configuration space to identify different connected components within it and assess the connectivity of the start and goal cells. We illustrate this methodology on five different scenarios with configuration spaces having up to 5 degrees-of-freedom (DOF). Additionally, we discuss further optimizations designed to significantly accelerate the proposed algorithm. The scalability of our approach to higher-dimensional configuration spaces is also examined, with experimental demonstrations involving 6-DOF and 7-DOF robots.

@inproceedings{bib_Benc_2026, AUTHOR = {Karami, Hossein and Thomas, Antony and Mastrogiovanni, Fulvio }, TITLE = {Benchmark evaluation in task and motion planning using iteratively deepened AND/OR graph networks}, BOOKTITLE = {Frontiers in Robotics and AI}. YEAR = {2026}}

In robotics research, each subdomain presents a distinct set of challenges, and any framework designed for a given domain must effectively address these complexities. However, a single application within that domain may not fully capture the breadth of challenges inherent to it. To enable systematic and comprehensive evaluation, the robotics community has developed standardized problem scenarios and associated performance metrics, commonly referred to as benchmarks, which collectively represent the diverse challenges arising across applications. In this work, we evaluate our task–motion Planning (TMP) framework on five benchmarks proposed by the TMP community. We begin by briefly describing our iterative deepening AND/OR graph–based TMP planner. Subsequently, we assess its performance across these benchmarks, each designed to capture different aspects of the challenges in TMP. The evaluation demonstrates that the proposed planner successfully solves all five benchmarks, thereby indicating that our framework constitutes a robust and effective solution for TMP.

@inproceedings{bib_SPOT_2026, AUTHOR = {Srivastava, Astik and Chackenkulam, Thomas J and Teja, Bitla Bhanu and Thomas, Antony and Krishna, K Madhava }, TITLE = {SPOT: Spatio-Temporal Trajectory Planning for UAVs in Unknown Dynamic Environments}, BOOKTITLE = {International Conference on Robotics and Automation}. YEAR = {2026}}

We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4-dimensional spatio-temporal planner, integrated with vision-based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision-based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real-world hardware experiments, and benchmark it against state-of-the-art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.

@inproceedings{bib_Loca_2025, AUTHOR = {Thomas, Antony and Mastrogiovanni, Fulvio and Baglietto, Marco }, TITLE = {Locally optimal solutions to constraint displacement problems via path-obstacle overlaps}, BOOKTITLE = {Robotics and Autonomous systems}. YEAR = {2025}}

We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. The first stage proceeds by computing a trajectory through the obstacles while minimizing an appropriate objective function. In the second stage, these obstacles are displaced to make the computed robot trajectory feasible, that is, collision-free. Several examples are provided that successfully demonstrate our approach on two distinct classes of constraint displacement problems.

@inproceedings{bib_A_ta_2025, AUTHOR = {Karami, Hossein and Thomas, Antony and Mastrogiovanni, Fulvio }, TITLE = {A task and motion planning framework using iteratively deepened AND/OR graph networks}, BOOKTITLE = {Robotics and Autonomous systems}. YEAR = {2025}}

In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a given number of robots, and (ii) can handle multi-robot tasks involving an a priori unknown number of sub-tasks. The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems.