Swarm and field robotics face significant barriers to real-world validation due to the high cost and development time to deploy hardware. This paper introduces the ``Bionic Swarm,'' a novel system that lowers these barriers by abstracting away many of the tasks that are difficult to implement on robots but which do not contribute to the overall algorithm evaluation, giving these tasks to human users. These human users take directions from a smartphone web-app that takes measurements from Bluetooth-connected sensors and relays them to a centralized server. This server runs the swarm algorithm and directs actions to the human users. We evaluate this system through the experimental validation of a geotechnically-focused search algorithm named Score-Biased-Search, which functions by assigning a ``score'' to each location on a reconstructed map, then biases search patterns through areas of higher expected scores, and which exhibits superlinear map reconstruction relative to the number of search agents. After presenting simulation results for the algorithm, we then apply the algorithm on the Bionic Swarm platform to validate its function in a real-world, outdoor setting. This work demonstrates that this human-in-the-loop approach significantly lowers the barrier to entry for field and swarm robotics research.

翻译：集群机器人与野外机器人技术因部署硬件的高成本与长开发周期，在实际场景验证中面临重大障碍。本文提出"仿生集群"这一新型系统，通过将诸多在机器人上难以实现且对整体算法评估无直接贡献的任务抽象化并分配给人类用户，有效降低了上述障碍。人类用户通过智能手机网页应用接收指令，该应用采集蓝牙连接传感器的测量数据并将其传输至中央服务器。服务器运行集群算法并指挥人类用户执行相应动作。我们通过实验验证了一种名为"评分偏置搜索"的地质聚焦搜索算法来评估该系统。该算法通过为重构地图上的每个位置赋予"评分"，使搜索模式偏向预期评分较高的区域，并展现出与搜索代理数量呈超线性关系的地图重构性能。在展示该算法的仿真结果后，我们将其应用于仿生集群平台，以验证其在真实户外环境中的功能。本研究证明，这种人机协同方法显著降低了野外机器人与集群机器人研究的准入门槛。