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Title: Constraint-Driven Optimal Control for Emergent Swarming and Predator Avoidance
In this article, 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. We map this to an equivalent switching system 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.  more » « less
Award ID(s):
2149520 2219761
NSF-PAR ID:
10421261
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2023 American Control Conference
Page Range / eLocation ID:
399-404
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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