2023/02/10 by Yuyang Zhang, Zhang, Yuyang, Meng Guo +1
Computer Science · #Advanced Software Engineering Methodologies #FOS: Computer and information sciences #FOS: Electrical engineering #Formal Methods in Verification #Robotic Path Planning Algorithms #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2302.05152
openalex publication_date 2023/02/10 · openalex created_date 2023/02/14 · openalex updated_date 2026/07/28
This paper addresses the online motion planning problem of mobile robots under complex high-level tasks. The robot motion is modeled as an uncertain Markov Decision Process (MDP) due to limited initial knowledge, while the task is specified as Linear Temporal Logic (LTL) formulas. The proposed framework enables the robot to explore and update the system model in a Bayesian way, while simultaneously optimizing the asymptotic costs of satisfying the complex temporal task. Theoretical guarantees are provided for the synthesized outgoing policy and safety policy. More importantly, instead of greedy exploration under the classic ergodicity assumption, a safe-return requirement is enforced such that the robot can always return to home states with a high probability. The overall methods are validated by numerical simulations.