Moving Obstacle Avoidance: A Data-Driven Risk-Aware Approach
Skylar X. Wei, Anushri Dixit, Shashank Tomar, Joel W. Burdick
Abstract
This letter proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories. The bootstrapped predictions are incorporated in the MPC optimization using a risk-aware methodology so as to provide probabilistic guarantees on obstacle avoidance. We validate our methods using simulations of a multi-rotor drone that avoids various moving obstacles.
Topics & Concepts
Obstacle avoidanceObstacleComputer scienceA priori and a posterioriProbabilistic logicTrajectoryDroneSet (abstract data type)Bootstrapping (finance)Artificial intelligenceScheme (mathematics)Model predictive controlControl theory (sociology)Control (management)MathematicsMobile robotRobotEconometricsEpistemologyLawPolitical scienceBiologyPhysicsGeneticsProgramming languageAstronomyMathematical analysisPhilosophyRobotic Path Planning AlgorithmsAdvanced Control Systems OptimizationGuidance and Control Systems