A mobile robot controller using reinforcement learning under scLTL specifications with uncertainties. Issue 6 (23rd November 2021)

• Record Type: Journal Article
• Authors: Mi, Jian; Kuze, Naomi; Ushio, Toshimitsu
• Is Part Of: Asian journal of control. Volume 24:Issue 6(2022)
• Page Start: 2916
• Page End: 2930
• Publication Date: 2021-11-23
• Subjects: reinforcement learning -- RLwF -- scLTL specification -- uncertainties; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2712
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: The control of a mobile robot is a well‐studied problem in robotics that can be solved easily when performing simple tasks without uncertainties. However, uncertainties always exist when a mobile robot performs a series of tasks in the real‐world. In this paper, we proposed a reinforcement learning (RL)‐based controller for a mobile robot to perform a task with uncertainties. We consider the case where a task consists of several subtasks described by syntactically co‐safe linear temporal logic (scLTL) specifications and each scLTL specification is transformed into a finite state automaton (FSA) that accepts all behavior satisfying the scLTL specification. A reinforcement learning with an FSA‐encoder (RLwF) method is proposed to learn an optimal control policy in performing tasks in an environment with uncertainties. We propose an RL‐based control method with uncertainties to learn rapidly an optimal policy against the uncertainties such as jammers that prevent a mobile robot from performing specified tasks. By simulation, we demonstrate that the proposed controller can learn an optimal policy and generate an optimal path to perform the designed tasks.