C-Uniform Trajectory Sampling For Fast Motion Planning

TL;DR

This paper introduces C-Uniform trajectory sampling, a novel method that improves the coverage and efficiency of motion planning by generating more uniformly distributed samples in the configuration space.

Contribution

The paper proposes C-Uniformity for trajectory sampling, providing a closed-form solution for 1D cases and a network-flow based optimization for general systems, enhancing motion planning performance.

Findings

C-Uniform trajectories improve MPPI controller performance by up to 40%.

The method is validated through simulations and a practical scale racer implementation.

C-Uniform sampling achieves more uniform configuration space coverage.

Abstract

We study the problem of sampling robot trajectories and introduce the notion of C-Uniformity. As opposed to the standard method of uniformly sampling control inputs (which lead to biased samples of the configuration space), C-Uniform trajectories are generated by control actions which lead to uniform sampling of the configuration space. After presenting an intuitive closed-form solution to generate C-Uniform trajectories for the 1D random-walker, we present a network-flow based optimization method to precompute C-Uniform trajectories for general robot systems. We apply the notion of C-Uniformity to the design of Model Predictive Path Integral controllers. Through simulation experiments, we show that using C-Uniform trajectories significantly improves the performance of MPPI-style controllers, achieving up to 40% coverage performance gain compared to the best baseline. We demonstrate the practical applicability of our method with an implementation on a 1/10th scale racer.