Trajectory Planning
Trajectory planning focuses on generating optimal paths for robots and autonomous vehicles, considering factors like speed, acceleration, and collision avoidance. Current research emphasizes robust methods handling uncertainties in dynamic environments, employing techniques such as Partially Observable Markov Decision Processes (POMDPs), Bayesian games, and neural networks (including transformers and graph neural networks) for improved prediction and decision-making. These advancements are crucial for enhancing the safety, efficiency, and reliability of autonomous systems across diverse applications, from autonomous driving and multi-robot coordination to teleoperated space manipulators and advanced robotics.
Papers
Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms
Marwan Dhuheir, Emna Baccour, Aiman Erbad, Sinan Sabeeh Al-Obaidi, Mounir Hamdi
Perching on Moving Inclined Surfaces using Uncertainty Tolerant Planner and Thrust Regulation
Sensen Liu, Wenkang Hu, Zhaoying Wang, Wei Dong, Xinjun Sheng