Flexible Swarm: From Pursuit to Defense Without Reprogramming express
A wolf pack switches from chasing prey to defending its den in an instant. Engineers have instilled this same versatility into an algorithm for a robot swarm. The key is a method proposed by John von Neumann: instead of chasing the target, the robots calculate its future moves and block escape routes, like beaters cutting off trails. Paradoxically, to catch the target, they often slow down: this reduces unpredictability and makes pursuit inevitable.
An internal guardian, a simple mechanism, ensures that in the heat of the chase, no one collides. By tweaking numerical settings, the swarm seamlessly flows from hunting to impenetrable defense. No reprogramming is needed: the swarm transforms on the fly.
Unexpected fact: the same principles are already used to analyze crowd movements: they predict crushes at stadiums and open emergency exits in advance.
Original abstract
as written by the authors arXiv:2607.10986
This paper introduces a hierarchical control architecture for multi-agent adversarial environments, decoupling strategic task planning from rigorous safety assurance. The system formulates pursuit-evasion as a zero-sum receding-horizon game, solved via an iterative minimax \acl{mpc} scheme. This allows pursuers to anticipate and block evader trajectories using transverse velocity penalties rather than relying on reactive heuristic formations. To guarantee collision-free operation without compromising the convexity of the \acl{mpc}, a discrete-time \acl{cbf} operates as an inner-loop safety filter. Through simulated experiments, we demonstrate the framework's adaptability. By simply altering the weights of the shared zero-sum payoff and \acl{cbf} constraints, the swarm can fluidly switch from aggressive pursuit-evasion tactics to strict perimeter defense and area denial, demonstrating robust performance across varying rules of engagement without structural changes to the control logic. The source code is available: https://github.com/ananya-ac/pursuit-evasion-mpc-cbf.
A similar approach is used in autonomous driving systems: cars at an intersection 'negotiate' who goes first by predicting each other's actions.
In the TV series Stargate SG-1, the Replicators form a swarm that instantly reconfigures from attack to defense — almost like an embodiment of this algorithm.