  An integration of distributionally robust risk allocation into sampling-based
motion planning algorithms for robots operating in uncertain environments is
proposed. We perform non-uniform risk allocation by decomposing the
distributionally robust joint risk constraints defined over the entire planning
horizon into individual risk constraints given the total risk budget.
Specifically, the deterministic tightening defined using the individual risk
constraints is leveraged to define our proposed exact risk allocation
procedure. Our idea of embedding the risk allocation technique into sampling
based motion planning algorithms realises guaranteed conservative, yet
increasingly more risk feasible trajectories for efficient state space
exploration.
