ME-PATS: Mutually Enhancing Search-Based Planner and Learning-Based Agent for Tractor-Trailer Systems

Abstract

Planning a kinodynamically feasible path for a tractor-trailer vehicle is challenging for both search-based and learning-based methods due to the vehicle's unique kinematics and complex obstacles. These factors increase the likelihood of infeasible paths and exacerbate long-horizon issues. We introduce ME-PATS: a framework that mutually enhances the search-based planner and the learning-based agent for tractortrailer systems. The search-based planner provides successful trajectories to help the learning-based agent update its policy, while the agent improves the planner's efficiency through direct path simulation. Additionally, we propose two approaches to apply our framework to more challenging tasks: designing obstacle-aware networks to enhance the learning-based agents capabilities, and combining the planner's paths with the trained agent's simulated paths through multi-segment integration. Full details and results are available on our project website at https://github.com/FrankSinatral/TTsystems.