自主纳米蜂群探索的多传感器防碰撞设计
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
AI总结：
本文提出一种结合激光测距、视觉避障和UWB测距的多传感器防碰撞系统，将纳米蜂群任务成功率从20%提升至80%，并实现92.8%的机内防撞率。
摘要：
本文提出了一种多传感器防碰撞系统设计，以实现由10厘米级纳米无人机组成的蜂群在物体检测任务中的稳健自主探索能力。我们结合了轻量级单光束激光测距以避免近距离碰撞，基于视觉的远距离避障深度学习模型（即PULP-Dronet），以及基于超宽带（UWB）的测距模块以防止蜂群内部碰撞。实地研究表明，我们的多传感器方法能够防止与静态障碍物的碰撞，在杂乱环境中，相对于最先进的（SoA）基线，任务成功率从20%提高到80%。同时，基于UWB的子系统在四架无人机机队中，在65厘米的安全距离内防止无人机之间碰撞的成功率达到92.8%。在由GAP8多核处理器扩展的SoA机器人平台上，PULP-Dronet与物体检测任务交错运行，将其执行限制在1.6帧/秒。这一吞吐量足以以约40%的概率避免障碍物，但表明下一代纳米无人机蜂群需要更强大的处理器。
英文摘要：
This work presents a multi-sensory anti-collision system design to achieve robust autonomous exploration capabilities for a swarm of 10 cm-side nano-drones operating on object detection missions. We combine lightweight single-beam laser ranging to avoid proximity collisions with a long-range vision-based obstacle avoidance deep learning model (i.e., PULP-Dronet) and an ultra-wide-band (UWB) based ranging module to prevent intra-swarm collisions. An in-field study shows that our multisensory approach can prevent collisions with static obstacles, improving the mission success rate from 20% to 80% in cluttered environments w.r.t. a State-of-the-Art (SoA) baseline. At the same time, the UWB-based sub-system shows a 92.8% success rate in preventing collisions between drones of a four-agent fleet within a safety distance of 65 cm. On a SoA robotic platform extended by a GAP8 multi-core processor, the PULP-Dronet runs interleaved with an objected detection task, which constraints its execution at 1.6 frame/s. This throughput is sufficient for avoiding obstacles with a probability of about 40% but shows a need for more capable processors for the next-generation nano-drone swarms.
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。
