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As robots operate in increasingly complex and dynamic environments, fastmotion re-planning has become a widely explored area of research. In areal-world deployment, we often lack the ability to fully observe theenvironment at all times, giving rise to the challenge of determining how tobest perceive the environment given a continuously updated motion plan. Weprovide the first investigation into a `smart' controller for gaze control withthe objective of providing effective perception of the environment for obstacleavoidance and motion planning in dynamic and unknown environments. We detailthe novel problem of determining the best head camera behaviour for mobilerobots when constrained by a trajectory. Furthermore, we propose a greedyoptimisation-based solution that uses a combination of voxelised rewards andmotion primitives. We demonstrate that our method outperforms the benchmarkmethods in 2D and 3D environments, in respect of both the ability to explorethe local surroundings, as well as in a superior success rate of findingcollision-free trajectories -- our method is shown to provide 7.4x better mapexploration while consistently achieving a higher success rate for generatingcollision-free trajectories. We verify our findings on a physical Toyota HumanSupport Robot (HSR) using a GPU-accelerated perception framework.

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