In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the tradeoffs that exist between centralization and decentralization have not been thoroughly studied. In this paper, we investigate these tradeoffs for multi-robot coverage, and find that they are more nuanced than expected. For instance, our findings reinforce the expectation that more decentralized control will provide better scalability, but contradict the expectation that more decentralized control will perform better in environments with randomized obstacles. Beginning with a group of fully independent ground robots executing coverage, we add unmanned aerial vehicles as supervisors and progressively increase the degree to which the supervisors use centralized control, in terms of access to global information and a central coordinating entity. We compare, using the multi-robot physics-based simulation environment ARGoS, the following four control approaches: decentralized control, hybrid control, centralized control, and predetermined control. In comparing the ground robots performing the coverage task, we assess the speed and efficiency advantages of centralization -- in terms of coverage completeness and coverage uniformity -- and we assess the scalability and fault tolerance advantages of decentralization. We also assess the energy expenditure disadvantages of centralization due to different energy consumption rates of ground robots and unmanned aerial vehicles, according to the specifications of robots available off-the-shelf.

翻译：在群体机器人学中，分散控制常被提出作为集中控制更具可扩展性和容错性的替代方案。然而，集中式行为通常比分散式行为更快、更高效。在任何特定应用中，所执行任务的目标与约束应指导选择使用集中控制、分散控制或二者结合。目前，集中化与分散化之间的权衡尚未得到深入研究。本文针对多机器人覆盖任务探究了这些权衡，并发现其比预期更为微妙。例如，我们的发现强化了更分散的控制将提供更好可扩展性的预期，但反驳了更分散的控制在随机障碍物环境中表现更好的预期。我们从一个执行覆盖任务的完全独立的地面机器人群体开始，引入无人机作为监督者，并逐步提高监督者使用集中控制的程度，包括获取全局信息和中央协调实体的权限。我们使用基于物理的多机器人仿真环境ARGoS，比较了以下四种控制方法：分散控制、混合控制、集中控制和预定控制。在比较执行覆盖任务的地面机器人时，我们评估了集中化在速度与效率上的优势——以覆盖完整性和覆盖均匀性衡量——并评估了分散化在可扩展性和容错性上的优势。我们还根据现有商用机器人的规格，评估了集中化因地面机器人与无人机不同能耗率而导致的能量消耗劣势。