Collision Free Navigation with Interacting, Non-Communicating Obstacles

TL;DR

This paper presents a robust control algorithm enabling collision-free navigation among densely packed, non-communicating agents by using feedback from observed disturbances, with proven stability and performance guarantees for two-agent interactions.

Contribution

It introduces a novel control approach that accounts for uncommunicative agents through disturbance feedback, ensuring safe navigation without explicit communication.

Findings

Proven stability of the feedback loop for two agents.

Guarantees on constraint adherence during navigation.

Effective collision avoidance in dense, agent-populated environments.

Abstract

In this paper we consider the problem of navigation and motion control in an area densely populated with other agents. We propose an algorithm that, without explicit communication and based on the information it has, computes the best control action for all the agents and implements its own. Notably, the host agent (the agent executing the algorithm) computes the differences between the other agents' computed and observed control actions and treats them as known disturbances that are fed back into a robust control barrier function (RCBF) based quadratic program. A feedback loop is created because the computed control action for another agent depends on the previously used disturbance estimate. In the case of two interacting agents, stability of the feedback loop is proven and a performance guarantee in terms of constraint adherence is established. This holds whether the other agent executes the same algorithm or not.