用蚂蚁信息素机制让机器人集群高效避撞探索未知环境。
SPACE: Swarm Pheromone Fields for Adaptive Collision-Aware Exploration
- 模拟蚂蚁觅食，用信息素场实现去中心化协同
- 在256个机器人的密集场景下，碰撞率降低4至17倍
- 适合大规模地面机器人集群的避障探索任务
大规模机器人集群可快速探索未知环境，但增加机器人数量最终会因拥堵而失效。门道和密集交通导致频繁碰撞，降低每个新增机器人的价值。本文研究了数十到数百个地面机器人的安全-效率权衡问题。提出SPACE（Swarm Pheromone Fields for Adaptive Collision-Aware Exploration），受蚂蚁觅食启发，通过共享环境场实现协调：包含吸引前沿的信息素、排斥探索的信息素以及快速响应密度的信息素场。该方法为去中心化协作，基于该场进行决策。我们在真实建筑平面图上评估，使用HouseExpo数据集中的16个家庭布局和KTH数据集中的8个校园楼层，测试规模达256个机器人。SPACE位于实证帕累托前沿，在所有高密度集群中均达到最低的机器人间接触率，相比贪婪最近前沿规划器减少4至17倍，同时覆盖率时间仅比近似最优规划器高出约2%。结果表明，在此规模下，协调主要提升安全性而非缩短覆盖时间。
原文摘要 · Abstract (English)
Massive robot swarms can explore unknown environments quickly, but adding robots eventually stops helping. Doorways and dense traffic create congestion, increasing inter-robot contacts and reducing the value of each additional robot. We study this safety-efficiency tradeoff for ground swarms of tens to hundreds of robots. We present SPACE, Swarm Pheromone Fields for Adaptive Collision-Aware Exploration. Inspired by ant foraging, SPACE maintains a shared environmental field with an attractive frontier pheromone, a repellent explore pheromone, and a fast robot-density field. Coordination is decentralized and mediated through this field. We evaluate SPACE on real building floorplans, namely sixteen home layouts from the HouseExpo dataset and eight campus floors from the KTH dataset, with swarms of up to two hundred and fifty-six robots. SPACE lies on the empirical Pareto frontier. It attains the lowest inter-robot contact rate at every congested swarm size, four to seventeen times fewer than a greedy nearest-frontier planner, while keeping coverage time within about two percent of that near time-optimal planner. The results indicate that, at this scale, coordination mainly improves safety rather than coverage time.
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