用真人代替机器人执行任务，低成本实现野外土壤测绘。
Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
- 让真人通过手机应用配合传感器完成采集，由系统统一调度
- 算法在真实环境中实现超线性地图重建，搜索效率随人数提升更快
- 适合想快速验证群智算法的科研人员，降低硬件门槛
群集与田间机器人在真实场景中验证面临高昂成本和开发周期长的问题。本文提出‘仿生群集’（Bionic Swarm）系统，通过将机器人难以实现但不影响算法评估的任务交由人类用户完成，显著降低部署门槛。用户通过手机网页应用接收指令，使用蓝牙传感器采集数据并上传至中央服务器；服务器运行群集算法并指挥用户行动。我们以面向地质工程的评分偏置搜索（Score-Biased-Search）算法为例进行实验验证：该算法为地图上每个位置分配评分，并引导搜索向高分区域集中，表现出相对于搜索代理数量的超线性地图重建能力。先在仿真中验证算法性能，再在真实户外环境中通过Bionic Swarm平台完成实地测试。结果表明，该人机协同方法有效降低了田野与群集机器人研究的入门门槛。
原文摘要 · Abstract (English)
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.
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