Delayed Expansion AGT: Kinodynamic Planning with Application to Tractor-Trailer Parking

TL;DR

This paper introduces DE-AGT, a kinodynamic planning algorithm for articulated vehicles that uses delayed expansion of motion primitives, neural network heuristics, and improved goal-reaching to achieve faster autonomous parking in cluttered environments.

Contribution

The work presents a novel kinodynamic planning method combining delayed MP expansion, learned heuristics, and a new goal-reaching scheme for articulated vehicles.

Findings

Achieves 10x faster planning than previous methods

Uses neural networks for accurate cost-to-go prediction

Successfully applied to autonomous tractor-trailer parking

Abstract

Kinodynamic planning of articulated vehicles in cluttered environments faces additional challenges arising from high-dimensional state space and complex system dynamics. Built upon [1],[2], this work proposes the DE-AGT algorithm that grows a tree using pre-computed motion primitives (MPs) and A* heuristics. The first feature of DE-AGT is a delayed expansion of MPs. In particular, the MPs are divided into different modes, which are ranked online. With the MP classification and prioritization, DE-AGT expands the most promising mode of MPs first, which eliminates unnecessary computation and finds solutions faster. To obtain the cost-to-go heuristic for nonholonomic articulated vehicles, we rely on supervised learning and train neural networks for fast and accurate cost-to-go prediction. The learned heuristic is used for online mode ranking and node selection. Another feature of DE-AGT is the improved goal-reaching. Exactly reaching a goal state usually requires a constant connection checking with the goal by solving steering problems -- non-trivial and time-consuming for articulated vehicles. The proposed termination scheme overcomes this challenge by tightly integrating a light-weight trajectory tracking controller with the search process. DE-AGT is implemented for autonomous parking of a general car-like tractor with 3-trailer. Simulation results show an average of 10x acceleration compared to a previous method.