机器人集群的超轻量级协同建图
Ultra-Lightweight Collaborative Mapping for Robot Swarms
AI总结：
针对硬件受限的小型机器人集群建图难题，提出一种去中心化轻量级协同SLAM方法，在仅1.5MB内存的46克无人机上实现低于30厘米的建图精度，成本与计算需求降低两个数量级，支持数百智能体协同。
摘要：
机器人领域的一个关键要求是能够同时进行自定位和对未知环境建图，这主要依赖机载传感和计算。在高端机器人平台上实现完全机载的精确同步定位与建图（SLAM）是可行的，然而小型廉价机器人因硬件受限面临挑战，常需依赖外部基础设施进行传感与计算。在机器人集群中，这一挑战进一步加剧，因为协调、可扩展性和延迟是关键问题。本研究提出了一种去中心化的轻量级协同SLAM方法，使几乎任何机器人都能建图，即使是配备低成本硬件且仅有1.5 MB内存的设备，包括微型昆虫级设备。此外，所提方案支持大型集群编队，能够协调数百个智能体。为验证我们的主张，我们成功在重46克的厘米级无人机上实现了协同SLAM。值得注意的是，我们实现了低于30厘米的建图精度，该结果可与高端最先进方案相媲美，同时将成本、内存和计算需求降低了两个数量级。我们的方法在三个主要方面具有创新性。首先，在传感和计算方面，它通过轻量且高性价比（20美元）的方案实现了无需基础设施的机载协同建图。其次，我们优化了集群内的数据流量，以使用超宽带（UWB）、蓝牙或WiFi等标准无线协议支持数百个协同智能体。最后，我们实施了分布式集群协调策略，以降低建图延迟并提高精度。
英文摘要：
A key requirement in robotics is the ability to simultaneously self-localize and map a previously unknown environment, relying primarily on onboard sensing and computation. Achieving fully onboard accurate simultaneous localization and mapping (SLAM) is feasible for high-end robotic platforms, whereas small and inexpensive robots face challenges due to constrained hardware, therefore frequently resorting to external infrastructure for sensing and computation. The challenge is further exacerbated in swarms of robots, where coordination, scalability, and latency are crucial concerns. This work introduces a decentralized and lightweight collaborative SLAM approach that enables mapping on virtually any robot, even those equipped with low-cost hardware and only 1.5 MB of memory, including miniaturized insect-size devices. Moreover, the proposed solution supports large swarm formations with the capability to coordinate hundreds of agents. To substantiate our claims, we have successfully implemented collaborative SLAM on centimeter-size drones weighing 46 g. Remarkably, we achieve a mapping accuracy below 30 cm, a result comparable to high-end state-of-the-art solutions while reducing the cost, memory, and computation requirements by two orders of magnitude. Our approach is innovative in three main aspects. First, it enables onboard infrastructure-less collaborative mapping with a lightweight and cost-effective (\$20) solution in terms of sensing and computation. Second, we optimize the data traffic within the swarm to support hundreds of cooperative agents using standard wireless protocols such as ultra-wideband (UWB), Bluetooth, or WiFi. Last, we implement a distributed swarm coordination policy to decrease mapping latency and enhance accuracy.
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。
