Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach

Autor:	Wei, Skylar X., Dixit, Anushri, Tomar, Shashank, Burdick, Joel W.
Rok vydání:	2022
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	This paper 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 3-dimensional multi-rotor drone that avoids various moving obstacles, such as a thrown ball and a frisbee with air drag. Comment: This is prepared for IEEE Control Systems Letters (L-CSS) 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.14913 Zobrazit plný text záznamu View this record from Arxiv