Stable generalized predictive control with constraints and bounded disturbances
Automatica (Journal of IFAC)
On motion planning in changing, partially predictable environments
International Journal of Robotics Research
Robot Motion Planning
Global Path Planning in Gaussian Probabilistic Maps
Journal of Intelligent and Robotic Systems
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Planning Algorithms
Real-time hierarchical POMDPs for autonomous robot navigation
Robotics and Autonomous Systems
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Stochastic mobility-based path planning in uncertain environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
Monte Carlo Optimization for Conflict Resolution in Air Traffic Control
IEEE Transactions on Intelligent Transportation Systems
Brief A probabilistically constrained model predictive controller
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Chance-Constrained Optimal Path Planning With Obstacles
IEEE Transactions on Robotics
Robot Motion Planning in Dynamic, Uncertain Environments
IEEE Transactions on Robotics
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The computationally efficient search for robust feasible paths for unmanned aerial vehicles (UAVs) in the presence of uncertainty is a challenging and interesting area of research. In uncertain environments, a "conservative" planner may be required but then there may be no feasible solution. In this paper, we use a chance constraint to limit the probability of constraint violation and extend this framework to handle uncertain dynamic obstacles. The approach requires the satisfaction of probabilistic constraints at each time step in order to guarantee probabilistic feasibility. The rapidly-exploring random tree (RRT) algorithm, which enjoys the computational benefits of a sampling-based algorithm, is used to develop a real-time probabilistically robust path planner. It incorporates the chance constraint framework to account for uncertainty within the formulation and includes a number of heuristics to improve the algorithm's performance. Simulation results demonstrate that the proposed algorithm can be used for efficient identification and execution of probabilistically safe paths in real-time.