Improved Reinforcement Learning Task Supervisor for Path Planning of Logistics Autonomous System

This study investigates the multi-objective path planning problem in logistics autonomous systems (LAS), where unmanned ground vehicles (UGVs) need to deliver multiple packages to various destinations while avoiding obstacles. Using the null-space-based behavioral control (NSBC) framework, we extend our previous reinforcement learning task supervisor (RLTS) to propose an enhanced RLTS (IRLTS) for optimal path planning and dynamic, simultaneous task priority adjustment. Notably, IRLTS can re-order delivery to minimize total path length when presented with unknown obstacles. Simulations affirm that IRLTS achieves shorter total path lengths than RLTS and outperforms offline optimization-based algorithms on path length and re-planning capability.