Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments

TL;DR

This paper presents an integrated system combining real-time environment reconstruction and whole-body motion planning, enabling safe, dynamic obstacle avoidance in unknown environments for robots.

Contribution

The work introduces a hybrid mapping and motion planning system that effectively uses GPU-Voxels and GPMP2 for real-time, dynamic obstacle avoidance in 3D space.

Findings

Distance field type impacts optimization convergence and error.

GPU-Voxels outperforms other methods in real-time reconstruction.

System successfully tested on Toyota HSR in simulation and real-world.

Abstract

Recent work has demonstrated real-time mapping and reconstruction from dense perception, while motion planning based on distance fields has been shown to achieve fast, collision-free motion synthesis with good convergence properties. However, demonstration of a fully integrated system that can safely re-plan in unknown environments, in the presence of static and dynamic obstacles, has remained an open challenge. In this work, we first study the impact that signed and unsigned distance fields have on optimisation convergence, and the resultant error cost in trajectory optimisation problems in 2D path planning, arm manipulator motion planning, and whole-body loco-manipulation planning. We further analyse the performance of three state-of-the-art approaches to generating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in real-time environment reconstruction. Finally, we use our findings to construct a practical hybrid mapping and motion planning system which uses GPU-Voxels and GPMP2 to perform receding-horizon whole-body motion planning that can smoothly avoid moving obstacles in 3D space using live sensor data. Our results are validated in simulation and on a real-world Toyota Human Support Robot (HSR).