基于Swarm Map的贝叶斯优化快速适应机器人集群
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
AI总结：
针对集群机器人应对环境扰动的性能恢复难题，提出SMBO和SMBO-Dec两种基于Swarm Map的贝叶斯优化算法，通过搜索控制器存档实现快速行为适应，在多种干扰实验中最多30次评估内性能提升达80%。
摘要：
从不可预见的环境扰动中快速恢复性能仍然是集群机器人领域的一项重大挑战。为了解决这一挑战，我们研究了一种行为适应方法，即通过搜索控制器存档来寻找潜在的恢复方案。为了将行为适应应用于集群机器人系统，我们提出了两种算法：(i) Swarm Map-based Optimisation (SMBO，基于Swarm Map的优化)，它以集中式方式，为同质集群每次选择并评估一个控制器；以及 (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec，基于Swarm Map的去中心化优化)，它执行基于异步批次的贝叶斯优化，为集群中的机器人组同时探索不同的控制器。我们设置了包含各种干扰的觅食实验：对单个机器人的接近传感器、地面传感器和执行器注入故障，每种类型有100种独特组合。我们还研究了集群运行环境中的干扰，其中集群必须适应环境中可用资源数量的剧烈变化，以及其中一个机器人对集群其余部分表现出破坏性行为的情况，每种此类扰动有30种独特条件。实验证明了SMBO和SMBO-Dec的可行性，与随机搜索和梯度下降的变体以及各种消融实验相比表现更优，并且在最多30次评估内，与故障注入时的性能相比，性能提升高达80%。
英文摘要：
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. To apply behaviour adaptation in swarm robotic systems, we propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. We set up foraging experiments with a variety of disturbances: injected faults to proximity sensors, ground sensors, and the actuators of individual robots, with 100 unique combinations for each type. We also investigate disturbances in the operating environment of the swarm, where the swarm has to adapt to drastic changes in the number of resources available in the environment, and to one of the robots behaving disruptively towards the rest of the swarm, with 30 unique conditions for each such perturbation. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within at most 30 evaluations.
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