Joint Sampling and Trajectory Optimization over Graphs for Online Motion Planning

TL;DR

This paper introduces a unified online motion planning method combining sampling and trajectory optimization, effectively handling dynamic environments with high-dimensional constraints and outperforming traditional approaches.

Contribution

The paper presents a novel integrated approach that interleaves sampling and optimization for real-time motion planning in dynamic, high-dimensional environments.

Findings

Significantly better performance on multiple metrics compared to baselines.

Effective handling of dynamic environments with long planning horizons.

Outperforms methods using only sampling or only optimization.

Abstract

Among the most prevalent motion planning techniques, sampling and trajectory optimization have emerged successful due to their ability to handle tight constraints and high-dimensional systems, respectively. However, limitations in sampling in higher dimensions and local minima issues in optimization have hindered their ability to excel beyond static scenes in offline settings. Here we consider highly dynamic environments with long horizons that necessitate a fast online solution. We present a unified approach that leverages the complementary strengths of sampling and optimization, and interleaves them both in a manner that is well suited to this challenging problem. With benchmarks in multiple synthetic and realistic simulated environments, we show that our approach performs significantly better on various metrics against baselines that employ either only sampling or only optimization. Project page: https://sites.google.com/view/jistplanner