Tabu temporal difference learning for robot path planning in uncertain environments

Abstract

This paper addresses the robot path planning problem in uncertain environments, where the robot has to avoid potential collisions with other agents or obstacles, as well as rectify actuation errors caused by environmental disturbances. This problem is motivated by many practical applications, such as ocean exploration by underwater vehicles, and package transportation in a warehouse by mobile robots. The novel feature of this paper is that we propose a Tabu methodology consisting of an Adaptive Action Selection Rule and a Tabu Action Elimination Strategy to improve the classic Temporal Difference (TD) learning approach. Furthermore, two classic TD learning algorithms (i.e., Q-learning and SASRA) are revised by the proposed Tabu methodology for optimizing learning performance. We use a simulated environment to evaluate the proposed algorithms. The results show that the proposed approach can provide an effective solution for generating collision-free and safety paths for robots in uncertain environments.