Journal article
Optimal Probabilistic Motion Planning with Potential Infeasible LTL Constraints
IEEE transactions on automatic control, Vol.68(1), pp.301-316
01/2023
DOI: 10.1109/TAC.2021.3138704
Abstract
This paper studies optimal motion planning subject to motion and environment uncertainties. By modeling the system as a probabilistic labeled Markov decision process (PL-MDP), the control objective is to synthesize a finite-memory policy, under which the agent satisfies high-level complex tasks expressed as linear temporal logic (LTL) with desired satisfaction probability. In particular, the cost optimization of the trajectory that satisfies infinite-horizon tasks is considered, and the trade-off between reducing the expected mean cost and maximizing the probability of task satisfaction is analyzed. Instead of using traditional Rabin automata, the LTL formulas are converted to limit-deterministic Bchi automata (LDBA) with a more straightforward accepting condition and a more compact graph structure. The novelty of this work lies in the consideration of the cases that LTL specifications can be potentially infeasible and the development of a relaxed product MDP between PL-MDP and LDBA. The relaxed product MDP allows the agent to revise its motion plan whenever the task is not fully feasible and to quantify the violation measurement of the revised plan. A multi-objec
Details
- Title: Subtitle
- Optimal Probabilistic Motion Planning with Potential Infeasible LTL Constraints
- Creators
- Mingyu Cai - Mechanical Engineering, Lehigh University, Bethlehem, Pennsylvania, United States of America, 18018 (e-mail: mic221@lehigh.edu)Shaoping Xiao - Mechanical Engineering, University of Iowa, Iowa City, IA, United States of America, 52242 (e-mail: shaoping-xiao@uiowa.edu)Zhijun Li - Automation, USTC, Hefei, China, 230060 (e-mail: zjli@ieee.org)Zhen Kan - Department of Automation, University of Science and Technology of China, Hefei, Anhui, China, 230026 (e-mail: zkan@ustc.edu.cn)
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on automatic control, Vol.68(1), pp.301-316
- Publisher
- IEEE
- DOI
- 10.1109/TAC.2021.3138704
- ISSN
- 0018-9286
- eISSN
- 1558-2523
- Grant note
- DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 62173314, U2013601, 61625303
- Language
- English
- Electronic publication date
- 12/27/2021
- Date published
- 01/2023
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984209494302771
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