@article{xu2023TNNLS,
  title={Online and Robust Intermittent Motion Planning in Dynamic and Changing Environments},
  abstract = {In this paper, we propose RRT-Q$^{\textrm{X}}_{\infty}$, an online and intermittent kinodynamic motion planning framework for dynamic environments with unknown robot dynamics and unknown disturbances. We leverage RRT$^{\textrm{X}}$ for global path planning and rapid replanning to produce waypoints as a sequence of boundary value problems (BVPs). For each BVP, we formulate a finite-horizon, continuous-time zero-sum game, where the control input is the minimizer, and the worst-case disturbance is the maximizer. We propose a \textit{robust intermittent Q-learning} controller for waypoint navigation with completely unknown system dynamics, external disturbances, and intermittent control updates. We execute a relaxed persistence of excitation technique to guarantee that the Q-learning controller converges to the optimal controller. We provide rigorous Lyapunov-based proofs to guarantee the closed-loop stability of the equilibrium point. The effectiveness of the proposed RRT-Q$^{\textrm{X}}_{\infty}$ is illustrated with Monte-Carlo numerical experiments in numerous dynamic and changing environments.},
  author={Xu, Zirui and Kontoudis, George P and Vamvoudakis, Kyriakos G},
  journal={IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},
  year={2023},
  keywords={motion planning, reinforcement learning, optimal control, event-trigger control, game theory},
  url_pdf= {/publications/TNNLS23_Xu_OnlineRobustIntermittentMotionPlanningDynamicChangingEnvironments.pdf},
  url_video = {https://youtu.be/iS_PzDmlpfs},
  doi = {10.1109/TNNLS.2023.3303811}
}

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