On motion planning in changing, partially predictable environments
International Journal of Robotics Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Planning Algorithms
Intentional motion on-line learning and prediction
Machine Vision and Applications
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Analytic moment-based Gaussian process filtering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
A Bayesian nonparametric approach to modeling motion patterns
Autonomous Robots
Iterative MILP methods for vehicle-control problems
IEEE Transactions on Robotics
A review of conflict detection and resolution modeling methods
IEEE Transactions on Intelligent Transportation Systems
Spline-Based RRT Path Planner for Non-Holonomic Robots
Journal of Intelligent and Robotic Systems
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This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in real-time while significantly reducing the risk of collisions with dynamic obstacles.