Efficient Path Planning in Large Unknown Environments with Switchable System Models for Automated Vehicles

TL;DR

This paper introduces a novel path planning method for large, unknown environments that reduces computation time by limiting planning scope, detects environment changes for efficient replanning, and extends to a vehicle model with switchable system modes.

Contribution

It presents a new approach that limits path planning scope, detects environment changes, and enables system model switching for improved efficiency and maneuverability in automated vehicles.

Findings

Paths are nearly identical to standard Hybrid A* but with less computation.

The method significantly reduces execution time in large environments.

The extended planner enables efficient navigation in narrow spaces.

Abstract

Large environments are challenging for path planning algorithms as the size of the configuration space increases. Furthermore, if the environment is mainly unexplored, large amounts of the path are planned through unknown areas. Hence, a complete replanning of the entire path occurs whenever the path collides with newly discovered obstacles. We propose a novel method that stops the path planning algorithm after a certain distance. It is used to navigate the algorithm in large environments and is not prone to problems of existing navigation approaches. Furthermore, we developed a method to detect significant environment changes to allow a more efficient replanning. At last, we extend the path planner to be used in the U-Shift concept vehicle. It can switch to another system model and rotate around the center of its rear axis. The results show that the proposed methods generate nearly identical paths compared to the standard Hybrid A* while drastically reducing the execution time. Furthermore, we show that the extended path planning algorithm enables the efficient use of the maneuvering capabilities of the concept vehicle to plan concise paths in narrow environments.