  We integrate learning and motion planning for soccer playing differential
drive robots using Bayesian optimisation. Trajectories generated using
end-slope cubic Bezier splines are first optimised globally through Bayesian
optimisation for a set of candidate points with obstacles. The optimised
trajectories along with robot and obstacle positions and velocities are stored
in a database. The closest planning situation is identified from the database
using k-Nearest Neighbour approach. It is further optimised online through
reuse of prior information from previously optimised trajectory. Our approach
reduces computation time of trajectory optimisation considerably. Velocity
profiling generates velocities consistent with robot kinodynamoic constraints,
and avoids collision and slipping. Extensive testing is done on developed
simulator, as well as on physical differential drive robots. Our method shows
marked improvements in mitigating tracking error, and reducing traversal and
computational time over competing techniques under the constraints of
performing tasks in real time.
