@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.