Data from: Using collision cones to assess biological deconfliction methods

Biological systems consistently outperform autonomous systems governed by engineered algorithms in their ability to reactively avoid collisions. To better understand this discrepancy, a collision avoidance algorithm was applied to frames of digitized video trajectory data from bats, swallows and fish (Myotis velifer, Petrochelidon pyrrhonota and Danio aequipinnatus). Information available from visual cues, specifically relative position and velocity, was provided to the algorithm which used this information to define collision cones that allowed the algorithm to find a safe velocity requiring minimal deviation from the original velocity. The subset of obstacles provided to the algorithm was determined by the animal's sensing range in terms of metric and topological distance. The algorithmic calculated velocities showed good agreement with observed biological velocities, indicating that the algorithm was an informative basis for comparison with the three species and could potentially be improved for engineered applications with further study.

生物系统在反应式避障能力上始终优于基于工程化算法构建的自主系统。为更好地理解这一性能差异，研究人员将一款避障算法 (collision avoidance algorithm) 应用于蝙蝠、燕子与鱼类的数字化视频轨迹数据帧，涉及物种分别为墨西哥游离尾蝠（Myotis velifer）、红额燕（Petrochelidon pyrrhonota）与长鳍斑马鱼（Danio aequipinnatus）。
研究人员将视觉线索提供的相关信息（具体为相对位置与速度）输入该算法，算法利用这些信息构建碰撞锥 (collision cones)，进而求得与原运动速度偏差最小的安全运动速度。算法所采用的障碍物子集，由该动物在度量距离与拓扑距离维度下的感知范围所决定。
算法计算得到的运动速度与观测到的生物运动速度吻合度较高，这表明该算法可作为对比上述三个物种的有效基准，且经进一步研究后，有望在工程应用场景中得到优化。