Scenario-Based Motion Planning with Bounded Probability of Collision

TL;DR

This paper introduces Safe Horizon MPC, a real-time motion planning method that explicitly constrains the joint probability of collision, reducing conservativeness and improving safety in human-robot shared environments.

Contribution

The paper presents a novel scenario-based MPC approach that explicitly constrains joint collision probability, addressing limitations of existing chance-constrained methods.

Findings

Less conservative than existing methods

Applicable to arbitrary obstacle trajectory distributions

Demonstrated on mobile robot and autonomous vehicle

Abstract

Robots will increasingly operate near humans that introduce uncertainties in the motion planning problem due to their complex nature. Typically, chance constraints are introduced in the planner to optimize performance while guaranteeing probabilistic safety. However, existing methods do not consider the actual probability of collision for the planned trajectory, but rather its marginalization, that is, the independent collision probabilities for each planning step and/or dynamic obstacle, resulting in conservative trajectories. To address this issue, we introduce a novel real-time capable method termed Safe Horizon MPC, that explicitly constrains the joint probability of collision with all obstacles over the duration of the motion plan. This is achieved by reformulating the chance-constrained planning problem using scenario optimization and predictive control. Our method is less conservative than state-of-the-art approaches, applicable to arbitrary probability distributions of the obstacles' trajectories, computationally tractable and scalable. We demonstrate our proposed approach using a mobile robot and an autonomous vehicle in an environment shared with humans.