A fly inspired solution to looming detection for collision avoidance

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 Drosophila. 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.

Automated summary

In this study, we developed a computational model inspired by the looming detection circuit recently identified in Drosophila. Our results suggest that the key for accurate detection in the fly circuit 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, shedding light on the potential of the concise fly algorithm in real-time applications.