论文标题
迅速适应机器人群，基于群体地图的贝叶斯优化
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
论文作者
论文摘要
在不可预见的环境扰动中迅速恢复性能仍然是群体机器人技术的巨大挑战。为了解决这一挑战，我们研究了一种行为适应方法，在该方法中，人们搜索了控制器的档案，以寻求潜在的恢复解决方案。为了在群体机器人系统中应用行为适应，我们提出了两种算法：（i）基于群体地图的优化（SMBO），该算法一次选择并评估一个控制器，以一种集中式的群体； （ii）基于群体地图的优化分散化（SMBO-DEC），它执行基于批处理的贝叶斯优化，同时探索群体中机器人组的不同控制器。我们设置了具有多种干扰的觅食实验：注入邻近传感器，接地传感器和单个机器人的执行器，每种类型的100个独特组合。我们还调查了群体的操作环境中的干扰，群体必须适应环境中可用资源数量的急剧变化，以及一个机器人在群中的破坏性行为，每种这种扰动都有30个独特的条件。证明了SMBO和SMBO-DEC的生存能力，与随机搜索和梯度下降以及各种消融的变体相比，与在最多30次评估中进行故障注射时的性能相比，将性能提高到80％。
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.
