M-SET：具有避碰真实性的多无人机群智能实验
M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism
AI总结：
针对现有测试平台无法模拟碰撞、缺乏室外环境真实性的局限，本文提出多无人机感知实验测试平台M-SET，集成势场算法避碰与多智能体集体学习优化，实现无碰撞导航感知，经评估能耗估计准确、碰撞风险低，能低成本支持群智能分布式感知研究。
摘要：
协作无人机群的分布式感知对于交通监控、灾害响应等多个智慧城市应用至关重要。利用配备廉价无人机的室内实验室，测试平台可支持对这些系统的复杂且前沿研究，同时保持低成本、严谨性和外部有效性。本文介绍了多无人机感知实验测试平台（M-SET），这是一个用于原型设计、开发、测试和评估群智能分布式感知的新型平台。M-SET解决了现有测试平台无法模拟碰撞、因而缺乏室外环境真实性的局限性。通过集成基于势场算法的避碰方法，M-SET确保无碰撞导航和感知，并通过多智能体集体学习算法进一步优化。广泛评估表明，该平台能准确估计能耗且碰撞风险低，提供了可靠的概念验证。新见解显示，M-SET有潜力以最低成本、简单性和高感知质量支持前沿研究。
英文摘要：
Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
