Constraint-Driven Optimal Control for Emergent Swarming and Predator Avoidance

TL;DR

This paper introduces a decentralized optimal control framework for emergent flocking and predator avoidance in 2D environments, emphasizing safety and efficiency through constraint relaxation and finite state machine modeling.

Contribution

It presents a novel constraint-driven control method that explicitly handles safety, flocking, and predator avoidance, with a unique focus on minimizing agents' deviation from optimal speed.

Findings

Successful simulation of flocking behavior

Effective predator avoidance demonstrated

Constraint relaxation improves system robustness

Abstract

In this letter, we present a constraint-driven optimal control framework that achieves emergent cluster flocking within a constrained 2D environment. We formulate a decentralized optimal control problem that includes safety, flocking, and predator avoidance constraints. We explicitly derive conditions for constraint compatibility and propose an event-driven constraint relaxation scheme, which we map to an equivalent finite state machine that intuitively describes the behavior of each agent in the system. Instead of minimizing control effort, as it is common in the ecologically-inspired robotics literature, in our approach, we minimize each agent's deviation from their most efficient locomotion speed. Finally, we demonstrate our approach in simulation both with and without the presence of a predator.