Chance-constrained multi-layered sampling-based path planning for temporal logic-based missionsChance-Constrained Multilayered Sampling-Based Path Planning for Temporal Logic-Based Missions
- Other Titles
- Chance-Constrained Multilayered Sampling-Based Path Planning for Temporal Logic-Based Missions
- Authors
- oh, yoonseon; Cho, Kyunghoon; Choi, Yunho; Oh, Songhwai
- Issue Date
- Dec-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Linear temporal logic (LTL); Path planning; Probabilistic guarantee
- Citation
- IEEE TRANSACTIONS ON AUTOMATIC CONTROL, pp.5816 - 5829
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON AUTOMATIC CONTROL
- Start Page
- 5816
- End Page
- 5829
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190686
- DOI
- 10.1109/TAC.2020.3044273
- ISSN
- 0018-9286
- Abstract
- This article 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 rapidly exploring random tree 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.
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