多机器人通过贝叶斯方法高效判断更安全区域，减少危险暴露。
Bayesian Decentralized Decision-making for Multi-Robot Systems: Sample-efficient Estimation of Event Rates
- 用共轭先验建模事件间隔，实现分布式不确定性估计
- 样本效率高，能快速收敛并降低危险事件暴露率
- 适合动态危险环境中的自适应采样与探索任务
群体机器人在危险环境中进行有效集体决策，需权衡探索、通信与个体不确定性估计。本文提出一种去中心化贝叶斯框架，使一群简单机器人能够识别两个区域中较安全的一个，每个区域的危险事件发生率由泊松过程描述且未知。机器人采用共轭先验，逐步预测事件时间间隔，并生成置信度估计以调整行为。仿真结果表明，该系统能持续选择正确区域，同时通过样本高效性显著降低危险事件暴露。相比基线启发式方法，本方法在安全性与收敛速度上表现更优。该场景可扩展现有集体决策基准测试集，方法适用于动态危险环境中的自适应风险感知采样与探索。
原文摘要 · Abstract (English)
Effective collective decision-making in swarm robotics often requires balancing exploration, communication and individual uncertainty estimation, especially in hazardous environments where direct measurements are limited or costly. We propose a decentralized Bayesian framework that enables a swarm of simple robots to identify the safer of two areas, each characterized by an unknown rate of hazardous events governed by a Poisson process. Robots employ a conjugate prior to gradually predict the times between events and derive confidence estimates to adapt their behavior. Our simulation results show that the robot swarm consistently chooses the correct area while reducing exposure to hazardous events by being sample-efficient. Compared to baseline heuristics, our proposed approach shows better performance in terms of safety and speed of convergence. The proposed scenario has potential to extend the current set of benchmarks in collective decision-making and our method has applications in adaptive risk-aware sampling and exploration in hazardous, dynamic environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。