Oh, Yoon seon Cho, K. Choi, Y. Oh, Songhwai 2024-01-19T13:30:17Z 2024-01-19T13:30:17Z 2021-09-02 2021-12 0018-9286 https://pubs.kist.re.kr/handle/201004/116105 This paper introduces a robust and safe path planning algorithm in order to satisfy mission requirements specified in linear temporal logic (LTL). When a path is planned to accomplish a mission, it is possible for a robot to fail to complete the mission or collide with obstacles due to noises and disturbances in the system. Hence, we need to find a robust path against possible disturbances. We introduce a robust path planning algorithm, which maximizes the probability of success in accomplishing a given mission by considering disturbances 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 have analyzed properties of the proposed algorithm theoretically and validated the robustness and safety of paths generated by the algorithm in simulation and experiments using a quadrotor. IEEE English Institute of Electrical and Electronics Engineers Inc. Chance-Constrained Multi-Layered Sampling-Based Path Planning for Temporal Logic-Based Missions Article 10.1109/TAC.2020.3044273 1 IEEE Transactions on Automatic Control, v.66, no.12, pp.5816 - 5829 IEEE Transactions on Automatic Control 66 12 5816 5829 N scie scopus 000725800500017 2-s2.0-85098790226 Automation & Control Systems Engineering, Electrical & Electronic Automation & Control Systems Engineering Article Computer circuits Probability Robot programming Robots Chance-constrained Collision with obstacle Failure Probability Linear temporal logic Mission requirements Probability of success Robust path planning Safe path planning Temporal logic Collision avoidance Heuristic algorithms linear temporal logic Path planning Planning probabilistic guarantee Probabilistic logic Robots Safety Trajectory