LGMD based neural network for automatic collision detection

Real-time collision detection in dynamic scenarios is a hard task if the algorithms used are based on conventional techniques of computer vision, since these are computationally complex and, consequently, time-consuming. On the other hand, bio-inspired visual sensors are suitable candidates for mobile robot navigation in unknown environments, due to their computational simplicity. The Lobula Giant Movement Detector (LGMD) neuron, located in the locust optic lobe, responds selectively to approaching objects. This neuron has been used to develop bio-inspired neural networks for collision avoidance. In this work, we propose a new LGMD model based on two previous models, in order to improve over them by incorporating other algorithms. To assess the real-time properties of the proposed model, it was applied to a real robot. Results shown that the LGMD neuron model can robustly support collision avoidance in complex visual scenarios.(undefined

Locust Flight Muscle Activity and Body Orientation in Response to Objects Moving within Different Backgrounds

Locusts are ideal model systems to study complex behaviours, such as flight responses to objects approaching on a collision course. Previous studies have described the flight muscle activity, wing kinematics and aerodynamic forces during collision avoidance behaviour of locusts flying in the wind tunnel. Neural recordings have revealed the influence of backgrounds on the responses of the descending contralateral movement detector (DCMD). Flow field backgrounds delay DCMD responses to looming stimuli that are known to evoke collision avoidance and muscle asynchrony during flight. Therefore, I hypothesize that the flow field background will delay behavioural responses and affect the timing of flight muscle activity. To test for this hypothesis, I placed a loosely-tethered flying locust inside a flight simulator, and presented visual stimuli composed of disks looming within different backgrounds. Concurrent electromyogram (EMG) and videos were recorded before and during the approach. My results show that against both simple and flow field backgrounds, the locust performed collision avoidance behaviours, by exiting the area defining the pre-response epoch, in most of the trials. The time of behaviour (TOB) varied among trials, and neither depressor asymmetry (DA) nor LM97 firing rate changed at TOB. The linear correlation between rotational degrees of freedom [RDOF, including roll (η), pitch(χ), and yaw(ψ)], or the changes in RDOF from the previous frame (ΔRDOF), and DA was calculated to investigate the relationship between depressor asymmetry and behaviours. In the presence of a simple background, more trials showed a significant correlation between RDOF and DA, compared to a flow field background. In the simple background, the time when the correlation between RDOF/ΔRDOF (η, Δη, Δχ, and Δψ) and DA became significant occurred during the interquartile range of TOB. In the flow field background, the correlation between certain RDOF/ΔRDOF (η, ψ, Δη) and DA became significant, while the correlation between DA and χ became insignificant, during the interquartile range of TOB. These results suggest that the background types affected the correlation between RDOF (or ΔRDOF) and DA, and the time when the significance of the correlation changes could be related to TOB