Improved A-Search Guided Tree for Autonomous Trailer Planning

Welcome! This website supplements our IROS 2022 submission, in which we presents a motion planning strategy that utilized improved A-Search Guided Tree to enable autonomous parking of a standard 3-trailer system with a car-like tractor.

This paper presents a motion planning strategy that utilizes the improved A-search guided tree to enable autonomous parking of a general 3-trailer with a car-like tractor. Different from the state-of-the-art state-lattice-based methods where numerous motion primitives are necessary to ensure successful planning, our work allows quick off-lattice exploration to find a solution. Our treatment brings at least three advantages: fewer and lower design complexity of motion primitives, improved success rate, and increased path quality. Unlike on-lattice exploration, where the cost-to-go is obtained by querying a heuristic look-up table, off-lattice exploration entails the heuristic function being well-defined at off-lattice nodes. We train a neural network through reinforcement learning to model the maneuver costs of the trailer and use it as the heuristic value to better approximate the cost-to-go. Simulations demonstrate the effectiveness of the proposed method in terms of planning speed and path length.