Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using Model Predictive Control

TL;DR

This paper introduces a Model Predictive Control framework for collision avoidance with dynamic obstacles under uncertainty, utilizing convex set representations and reformulations for different uncertainty models, validated through traffic intersection simulations.

Contribution

It presents a novel MPC approach that handles uncertain obstacle predictions with convex reformulations, enabling smooth collision avoidance constraints for polytopic obstacles.

Findings

Effective collision avoidance in uncertain environments demonstrated in simulations.

Convex reformulations enable smooth constraints without mixed-integer programming.

Feedback policies reduce conservatism in collision avoidance strategies.

Abstract

We propose a Model Predictive Control (MPC) for collision avoidance between an autonomous agent and dynamic obstacles with uncertain predictions. The collision avoidance constraints are imposed by enforcing positive distance between convex sets representing the agent and the obstacles, and tractably reformulating them using Lagrange duality. This approach allows for smooth collision avoidance constraints even for polytopes, which otherwise require mixed-integer or non-smooth constraints. We consider three widely used descriptions of the uncertain obstacle position: 1) Arbitrary distribution with polytopic support, 2) Gaussian distributions and 3) Arbitrary distribution with first two moments known. For each case we obtain deterministic reformulations of the collision avoidance constraints. The proposed MPC formulation optimizes over feedback policies to reduce conservatism in satisfying the collision avoidance constraints. The proposed approach is validated using simulations of traffic intersections in CARLA.