Integral Reinforcement Learning-Based Multi-Robot Minimum Time-Energy Path Planning Subject to Collision Avoidance and Unknown Environmental Disturbances

摘要

In this letter, we study the online multi-robot minimum time-energy path planning problem subject to collision avoidance and input constraints in an unknown environment. We develop an online adaptive solution for the problem using integral reinforcement learning (IRL). This is achieved through transforming the finite-horizon minimum time-energy problem with input constraints to an approximate infinite-horizon optimal control problem. To achieve collision avoidance, we incorporate artificial potential fields into the approximate cost function. We develop an IRL-based optimal control strategy and prove its convergence. The theoretical results are verified through simulation studies.