Spatial Constraint Generation for Motion Planning in Dynamic Environments

TL;DR

This paper introduces a new triangulation-based method for generating spatial constraints that improve motion planning stability and success in dynamic, unstructured environments for autonomous vehicles and robots.

Contribution

The paper proposes a novel sequence of channel generation across triangulation meshes to enhance motion planning in dynamic environments, outperforming existing methods.

Findings

More stable, long-term planning

Higher task completion rate

Fewer collisions

Abstract

This paper presents a novel method to generate spatial constraints for motion planning in dynamic environments. Motion planning methods for autonomous driving and mobile robots typically need to rely on the spatial constraints imposed by a map-based global planner to generate a collision-free trajectory. These methods may fail without an offline map or where the map is invalid due to dynamic changes in the environment such as road obstruction, construction, and traffic congestion. To address this problem, triangulation-based methods can be used to obtain a spatial constraint. However, the existing methods fall short when dealing with dynamic environments and may lead the motion planner to an unrecoverable state. In this paper, we propose a new method to generate a sequence of channels across different triangulation mesh topologies to serve as the spatial constraints. This can be applied to motion planning of autonomous vehicles or robots in cluttered, unstructured environments. The proposed method is evaluated and compared with other triangulation-based methods in synthetic and complex scenarios collected from a real-world autonomous driving dataset. We have shown that the proposed method results in a more stable, long-term plan with a higher task completion rate, faster arrival time, a higher rate of successful plans, and fewer collisions compared to existing methods.