ナノドローン群の自律探査のためのマルチセンサ衝突回避設計
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
10cm級ナノドローン群の自律探査に向け、レーザ測距・視覚ベース深層学習・UWB測距を組み合わせた衝突回避システムを構築し、実環境で成功率を20%から80%に改善した。
著者: Mahyar Pourjabar, Manuele Rusci, Luca Bompani, Lorenzo Lamberti, Vlad Niculescu, Daniele Palossi, Luca Benini
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
