Where Should I Look? Optimised Gaze Control for Whole-Body Collision Avoidance in Dynamic Environments

TL;DR

This paper introduces a novel gaze control strategy for mobile robots that optimizes environmental perception to improve obstacle avoidance and motion planning in dynamic, unknown environments, outperforming existing methods.

Contribution

It presents the first investigation into a smart gaze controller for mobile robots, using a greedy optimization approach with voxelised rewards and motion primitives.

Findings

7.4x better map exploration compared to benchmarks

Higher success rate in collision-free trajectory planning

Effective perception enhancement demonstrated on a physical robot

Abstract

As robots operate in increasingly complex and dynamic environments, fast motion re-planning has become a widely explored area of research. In a real-world deployment, we often lack the ability to fully observe the environment at all times, giving rise to the challenge of determining how to best perceive the environment given a continuously updated motion plan. We provide the first investigation into a `smart' controller for gaze control with the objective of providing effective perception of the environment for obstacle avoidance and motion planning in dynamic and unknown environments. We detail the novel problem of determining the best head camera behaviour for mobile robots when constrained by a trajectory. Furthermore, we propose a greedy optimisation-based solution that uses a combination of voxelised rewards and motion primitives. We demonstrate that our method outperforms the benchmark methods in 2D and 3D environments, in respect of both the ability to explore the local surroundings, as well as in a superior success rate of finding collision-free trajectories -- our method is shown to provide 7.4x better map exploration while consistently achieving a higher success rate for generating collision-free trajectories. We verify our findings on a physical Toyota Human Support Robot (HSR) using a GPU-accelerated perception framework.