Planning through Workspace Constraint Satisfaction and Optimization

TL;DR

This paper introduces a workspace-based planning framework that uses geometric information and optimization to generate collision-free paths for complex robots efficiently.

Contribution

It presents a novel method combining workspace key-points and optimization for fast, high-quality robot path planning.

Findings

The approach produces collision-free paths efficiently.

It leverages geometric workspace information for better planning.

The method is effective for complex robot configurations.

Abstract

In this work, we present a workspace-based planning framework, which though using redundant workspace key-points to represent robot states, can take advantage of the interpretable geometric information to derive good quality collision-free paths for even complex robots. Using workspace geometries, we first find collision-free piece-wise linear paths for each key point so that at the endpoints of each segment, the distance constraints are satisfied among the key points. Using these piece-wise linear paths as initial conditions, we can perform optimization steps to quickly find paths that satisfy various constraints and piece together all segments to obtain a valid path. We show that these adjusted paths are unlikely to create a collision, and the proposed approach is fast and can produce good quality results.