利用合作微无人机的最小感知探索无结构环境
Exploring Unstructured Environments using Minimal Sensing on Cooperative Nano-Drones
AI总结：
本文提出ExploreBug算法，通过最小感知能力实现微无人机群在无结构环境中的高效探索与制图。
摘要：
最近的进展提高了在载荷限制下的自主导航和制图能力，但当前的多机器人检查算法不适合于微无人机，因为它们需要重传感器和高计算资源。为了解决这些挑战，我们引入了ExploreBug，一种新的混合前沿范围虫算法，旨在处理有限的感知能力以处理无人机群。该系统包括三个主要组件：制图子系统、探索子系统和导航子系统。此外，还集成了一个群体内部碰撞避免系统，以防止无人机之间的碰撞。我们通过广泛的模拟和实际探索实验验证了我们方法的有效性，实验涉及最多七架无人机在模拟中和三架在实际环境中，各种障碍配置，并以最大导航速度0.75 m/s进行。我们的测试表明，该算法在不同无人机群大小和障碍密度下都能高效完成探索任务。此外，我们的前沿分配启发式方法确保了每个无人机在群体中探索的区域和路径的平均分配。我们公开发布所提出系统的源代码，以促进进一步利用自主微无人机进行制图和探索的发展。
英文摘要：
Recent advances have improved autonomous navigation and mapping under payload constraints, but current multi-robot inspection algorithms are unsuitable for nano-drones due to their need for heavy sensors and high computational resources. To address these challenges, we introduce ExploreBug, a novel hybrid frontier range bug algorithm designed to handle limited sensing capabilities for a swarm of nano-drones. This system includes three primary components: a mapping subsystem, an exploration subsystem, and a navigation subsystem. Additionally, an intra-swarm collision avoidance system is integrated to prevent collisions between drones. We validate the efficacy of our approach through extensive simulations and real-world exploration experiments involving up to seven drones in simulations and three in real-world settings, across various obstacle configurations and with a maximum navigation speed of 0.75 m/s. Our tests demonstrate that the algorithm efficiently completes exploration tasks, even with minimal sensing, across different swarm sizes and obstacle densities. Furthermore, our frontier allocation heuristic ensures an equal distribution of explored areas and paths traveled by each drone in the swarm. We publicly release the source code of the proposed system to foster further developments in mapping and exploration using autonomous nano drones.
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