  Over the years, the separate fields of motion planning, mapping, and human
trajectory prediction have advanced considerably. However, the literature is
still sparse in providing practical frameworks that enable mobile manipulators
to perform whole-body movements and account for the predicted motion of moving
obstacles. Previous optimisation-based motion planning approaches that use
distance fields have suffered from the high computational cost required to
update the environment representation. We demonstrate that GPU-accelerated
predicted composite distance fields significantly reduce the computation time
compared to calculating distance fields from scratch. We integrate this
technique with a complete motion planning and perception framework that
accounts for the predicted motion of humans in dynamic environments, enabling
reactive and pre-emptive motion planning that incorporates predicted motions.
To achieve this, we propose and implement a novel human trajectory prediction
method that combines intention recognition with trajectory optimisation-based
motion planning. We validate our resultant framework on a real-world Toyota
Human Support Robot (HSR) using live RGB-D sensor data from the onboard camera.
In addition to providing analysis on a publicly available dataset, we release
the Oxford Indoor Human Motion (Oxford-IHM) dataset and demonstrate
state-of-the-art performance in human trajectory prediction. The Oxford-IHM
dataset is a human trajectory prediction dataset in which people walk between
regions of interest in an indoor environment. Both static and robot-mounted
RGB-D cameras observe the people while tracked with a motion-capture system.
