让少数机器人牺牲计算量，提升整个集群的巡检效率。
Strategic Sacrifice: Self-Organized Robot Swarm Localization for Inspection Productivity
- 部分机器人主动承担定位任务，其他成员协作减少整体算力消耗。
- 在动态环境与复杂轨迹下，巡检效率提升显著，实验验证有效。
- 适用于金属爬行机器人等实际巡检场景，具强鲁棒性。
机器人集群在桥梁、空间站等多样化基础设施巡检中潜力巨大。然而高效巡检依赖精准定位，这需要大量计算资源，限制了整体效率。受生物系统启发，我们提出一种新型协作定位机制，通过自组织牺牲策略最小化集体计算开销：少数个体承担定位计算负担，经局部交互提升整个集群的巡检生产力。该方法在无约束轨迹、动态交互与环境变化下可自适应最大化巡检效率。我们通过均场分析模型、多智能体仿真及硬件实验（使用金属爬行机器人在三维圆柱表面巡检）验证了其最优性与鲁棒性。
原文摘要 · Abstract (English)
Robot swarms offer significant potential for inspecting diverse infrastructure, ranging from bridges to space stations. However, effective inspection requires accurate robot localization, which demands substantial computational resources and limits productivity. Inspired by biological systems, we introduce a novel cooperative localization mechanism that minimizes collective computation expenditure through self-organized sacrifice. Here, a few agents bear the computational burden of localization; through local interactions, they improve the inspection productivity of the swarm. Our approach adaptively maximizes inspection productivity for unconstrained trajectories in dynamic interaction and environmental settings. We demonstrate the optimality and robustness using mean-field analytical models, multi-agent simulations, and hardware experiments with metal climbing robots inspecting a 3D cylinder.
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