Our paper on robust path planning under LTL is accepted to CDC 2017

[2017.07.13]

The following paper is accepted to the IEEE Conference on Decision and Control (CDC 2017):

  • Robust Multi-Layered Sampling-Based Path Planning for Temporal Logic-Based Missions by Yoonseon Oh, Kyunghoon Cho, Yunho Choi, and Songhwai Oh
    • Abstract: We investigate a path planning algorithm for generating robust and safe paths, which satisfy mission requirements specified in linear temporal logic (LTL). When robots are deployed to perform a mission, there can be disturbances which can cause mission failures or collisions with obstacles. Hence, a path planning algorithm needs to consider safety and robustness against possible disturbances. We present a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances in robot dynamics while minimizing the moving distance of a robot. The proposed method can guarantee the safety of the planned trajectory by incorporating an LTL formula and chance constraints in a hierarchical manner. A high-level planner generates a discrete plan satisfying the mission requirements specified in LTL. A low-level planner builds a sampling-based RRT search tree to minimize both the mission failure probability and the moving distance while guaranteeing the probability of collision with obstacles to be below a specified threshold. We analyze properties of the proposed algorithm and validate the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor.
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