有限信息与安全保证下的自组织多智能体目标围捕
Self-organizing Multiagent Target Enclosing under Limited Information and Safety Guarantees
AI总结：
针对非完整多智能体目标围捕问题，提出结合势函数与滑模流形的自组织控制方法，仅需相对信息即可实现围捕与避碰，降低通信需求并通过仿真验证有效性。
摘要：
本文提出了一种利用非完整多智能体系统解决目标围捕问题的方法，其中智能体在固定目标周围的围捕形状上自组织。在我们的方法中，智能体在相距较远时独立向期望的围捕几何形状移动，并在碰撞即将发生时激活避碰机制，从而保证智能体间的安全。我们通过设计一种特殊的势函数和滑模流形，将全局围捕行为与局部避碰机制相结合。我们严格证明，智能体无需与其他所有智能体确保安全，并提出了（任意智能体的）最近碰撞智能体概念——只需与该智能体确保安全就足以避免整个群体中的碰撞。所提出的控制方法无需在目标周围设置固定或预先建立的智能体排列，仅需智能体与目标之间的相对信息。这使得我们的设计特别适用于全局信息有限的场景，从而显著降低了通信需求。最后，我们给出了仿真结果，验证了所提方法的有效性。
英文摘要：
This paper introduces an approach to address the target enclosing problem using non-holonomic multiagent systems, where agents self-organize on the enclosing shape around a fixed target. In our approach, agents independently move toward the desired enclosing geometry when apart and activate the collision avoidance mechanism when a collision is imminent, thereby guaranteeing inter-agent safety. Our approach combines global enclosing behavior and local collision avoidance mechanisms by devising a special potential function and sliding manifold. We rigorously show that an agent does not need to ensure safety with every other agent and put forth a concept of the nearest colliding agent (for any arbitrary agent) with whom ensuring safety is sufficient to avoid collisions in the entire swarm. The proposed control eliminates the need for a fixed or pre-established agent arrangement around the target and requires only relative information between an agent and the target. This makes our design particularly appealing for scenarios with limited global information, hence significantly reducing communication requirements. We finally present simulation results to vindicate the efficacy of the proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
