  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.
