Zhao Junyu, Xi Shengkai, Li Yan, Guo Aike, Wu Zhihua
State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
iScience. 2023 Mar 5;26(4):106337. doi: 10.1016/j.isci.2023.106337. eCollection 2023 Apr 21.
Dodging rapidly approaching objects is a fundamental skill for both animals and intelligent robots. Flies are adept at high-speed collision avoidance. However, it remains unclear whether the fly algorithm can be extracted and is applicable to real-time machine vision. In this study, we developed a computational model inspired by the looming detection circuit recently identified in . Our results suggest that in the face of considerably noisy local motion signals, the key for the fly circuit to achieve accurate detection is attributed to two computation strategies: population encoding and nonlinear integration. The model is further shown to be an effective algorithm for collision avoidance by virtual robot tests. The algorithm is characterized by practical flexibility, whose looming detection parameters can be modulated depending on factors such as the body size of the robots. The model sheds light on the potential of the concise fly algorithm in real-time applications.
躲避快速逼近的物体是动物和智能机器人的一项基本技能。苍蝇擅长高速避撞。然而，苍蝇的算法能否被提取并应用于实时机器视觉仍不清楚。在本研究中，我们开发了一种受最近发现的逼近检测电路启发的计算模型。我们的结果表明，面对相当嘈杂的局部运动信号，苍蝇电路实现精确检测的关键在于两种计算策略：群体编码和非线性整合。通过虚拟机器人测试进一步表明该模型是一种有效的避撞算法。该算法具有实际灵活性的特点，其逼近检测参数可根据机器人身体大小等因素进行调制。该模型揭示了简洁的苍蝇算法在实时应用中的潜力。
