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

2022-03-25 06:21:58
Skylar X. Wei, Anushri Dixit, Shashank Tomar, Joel W. Burdick

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2203.14913

PDF

https://arxiv.org/pdf/2203.14913.pdf


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