RRT-QX: Real-Time Kinodynamic Motion Planning in Dynamic Environments with Continuous-Time Reinforcement Learning. Kontoudis, G. P, Vamvoudakis, K. G, & Xu, Z. In Brain and Cognitive Intelligence: Control in Robotics. Taylor & Francis Group, CRC Press, 2022. Pdf Video doi abstract bibtex 7 downloads This chapter presents a real-time kinodynamic motion planning technique for linear systems with completely unknown dynamics in environments with unpredictable obstacles. The methodology incorporates: i) a sampling-based algorithm for path planning and fast replanning; and ii) continuous-time Q-learning for the solution of finite-horizon optimal control problems in real-time. The path planner produces a set of waypoints that dynamically change in time according to the unpredictably appearing obstacles, while the Q-learning controller is responsible for optimal waypoint navigation. The efficacy of the methodology has been validated with simulations.
@incollection{Kontoudis2022RRTQX,
title={RRT-QX: Real-Time Kinodynamic Motion Planning in Dynamic Environments with Continuous-Time Reinforcement Learning},
author={Kontoudis, George P and Vamvoudakis, Kyriakos G and Xu, Zirui},
editor = {Wei, Bin},
booktitle = {Brain and Cognitive Intelligence: Control in Robotics},
publisher = {Taylor \& Francis Group, CRC Press},
abstract = {This chapter presents a real-time kinodynamic motion planning technique for linear systems with completely unknown dynamics in environments with unpredictable obstacles. The methodology incorporates: i) a sampling-based algorithm for path planning and fast replanning; and ii) continuous-time Q-learning for the solution of finite-horizon optimal control problems in real-time. The path planner produces a set of waypoints that dynamically change in time according to the unpredictably appearing obstacles, while the Q-learning controller is responsible for optimal waypoint navigation. The efficacy of the methodology has been validated with simulations. },
year={2022},
keywords={motion planning, reinforcement learning, optimal control},
url_pdf = {Chap22_Kontoudis_RRTQX_RealTimeKinodynamicMotionPlanningDynamicEnvironments.pdf},
url_video = {https://youtu.be/Sxu04gSdsEA},
doi = {10.1201/9781003050315-1}
}
Downloads: 7
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