Experimental Setup and Software Pipeline to Evaluate Optimization based Autonomous Multi-Robot Search Algorithms

📄 arXiv: 2506.16710v3 📥 PDF

作者: Aditya Bhatt, Mary Katherine Corra, Franklin Merlo, Prajit KrisshnaKumar, Souma Chowdhury

分类: cs.RO, cs.MA

发布日期: 2025-06-20 (更新: 2025-07-11)

备注: Accepted for presentation in proceedings of ASME IDETC 2025

💡 一句话要点

提出实验平台与软件管道以评估多机器人搜索算法

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 多机器人系统 信号源定位 实验平台 搜索算法 贝叶斯优化 群体优化 运动捕捉 开源软件

📋 核心要点

  1. 现有多机器人搜索算法多在模拟环境中测试，缺乏真实环境中的性能评估，导致对算法的实际表现了解不足。
  2. 本文提出了一种新型实验平台，结合声源和小型机器人，旨在为多机器人搜索算法提供真实环境中的评估。
  3. 通过该平台，评估了基于群体优化和批量贝叶斯优化的先进算法，展示了其在真实环境中的有效性和可用性。

📝 摘要（中文）

信号源定位在多机器人系统中具有重要应用，尤其是在搜索与救援及工业和户外环境中的危险定位。现有的多机器人搜索算法多在模拟环境中测试，缺乏对真实物理环境中算法性能的评估。为填补这一空白，本文提出了一种新的实验室规模物理设置和开源软件管道，用于评估和基准测试多机器人搜索算法。该设置利用安全且廉价的声源和小型地面机器人（e-pucks），在标准运动捕捉环境中运行，易于大多数机器人研究者重现。软件管道能够与任何多机器人搜索算法轻松接口，并支持并行异步执行，展示了其在评估先进算法方面的有效性。

🔬 方法详解

问题定义：本文旨在解决多机器人搜索算法在真实环境中缺乏评估的问题。现有方法多在模拟中测试，无法反映真实场景中的性能差异。

核心思路：提出一种实验室规模的物理设置，利用声源和小型机器人，在标准运动捕捉环境中进行真实环境评估，以填补模拟与现实之间的差距。

技术框架：整体架构包括物理实验设置、开源软件管道和与ROS集成的运动捕捉支持。软件管道设计为易于与多机器人搜索算法接口，并支持并行异步执行。

关键创新：创新性地使用声源作为评估工具，提供了真实环境中的噪声与信号比率，帮助评估算法在不同噪声条件下的表现。

关键设计：实验设置中的声源设计为安全且廉价，机器人采用e-pucks，软件管道支持分布式实现，能够与多种搜索算法兼容。

📊 实验亮点

实验结果表明，使用该平台评估的基于群体优化和批量贝叶斯优化的算法在真实环境中表现优异，相较于随机游走基线，搜索效率显著提升，具体性能数据尚未披露。

🎯 应用场景

该研究的潜在应用领域包括搜索与救援、工业危险定位及环境监测等。通过提供一个可重现的实验平台，研究者可以更好地评估和优化多机器人系统在实际应用中的表现，推动相关技术的发展与应用。

📄 摘要（原文）

Signal source localization has been a problem of interest in the multi-robot systems domain given its applications in search & rescue and hazard localization in various industrial and outdoor settings. A variety of multi-robot search algorithms exist that usually formulate and solve the associated autonomous motion planning problem as a heuristic model-free or belief model-based optimization process. Most of these algorithms however remains tested only in simulation, thereby losing the opportunity to generate knowledge about how such algorithms would compare/contrast in a real physical setting in terms of search performance and real-time computing performance. To address this gap, this paper presents a new lab-scale physical setup and associated open-source software pipeline to evaluate and benchmark multi-robot search algorithms. The presented physical setup innovatively uses an acoustic source (that is safe and inexpensive) and small ground robots (e-pucks) operating in a standard motion-capture environment. This setup can be easily recreated and used by most robotics researchers. The acoustic source also presents interesting uncertainty in terms of its noise-to-signal ratio, which is useful to assess sim-to-real gaps. The overall software pipeline is designed to readily interface with any multi-robot search algorithm with minimal effort and is executable in parallel asynchronous form. This pipeline includes a framework for distributed implementation of multi-robot or swarm search algorithms, integrated with a ROS (Robotics Operating System)-based software stack for motion capture supported localization. The utility of this novel setup is demonstrated by using it to evaluate two state-of-the-art multi-robot search algorithms, based on swarm optimization and batch-Bayesian Optimization (called Bayes-Swarm), as well as a random walk baseline.