This paper presents a real-time path planning algorithm which can guarantee
probabilistic feasibility for autonomous robots subject to process noise and an
uncertain environment, including dynamic obstacles with uncertain motion
patterns. The key contribution of the work is the
integration of a novel method for modeling dynamic obstacles with uncertain future
trajectories. The method, denoted as RR-GP, uses a learned motion pattern model
of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the
flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach,
a sampling-based reachability computation method which ensures dynamic
feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random
trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic
constraint satisfaction while maintaining the computational
benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees
can be demonstrated for linear systems subject to Gaussian uncertainty,
though the extension to nonlinear systems is also considered. Simulation results
show that the resulting approach can be used in real-time to efficiently and
accurately execute safe paths
