Bio-inspired Neural Networks for Angular Velocity Estimation in Visually Guided Flights
Creators: name: Wang, Huatian
Publisher: name: University of Lincoln
Year: 2023
Subjects: G900 - Others in mathematical sciences

description: Executing delicate flight maneuvers using visual information is a huge challengefor future robotic vision systems. As a source of inspiration, insects are quite apt atnavigating in woods and landing on surfaces which require delicate visual perceptionand flight control. The exquisite sensitivity of insects for image motion speed, as revealed recently, is coming from a class of specific neurons called descending neurons.Some of the descending neurons have demonstrated angular velocity selectivity as theimage motion speed varies in retina. Build a quantitative angular velocity detectionmodel is the first step for not only further understanding of the biological visual system, but also providing robust and economic solutions of visual motion perceptionfor an artificial visual system. This thesis aims to explore biological image processingmethods for motion speed detection in visually guided flights. The major contributionsare summarized as follows.We have presented an angular velocity decoding model (AVDM), which estimatesthe visual motion speed combining both textural and temporal information from input signals. The model consists of three parts: elementary motion detection circuits,wide-field texture estimation pathway and angular velocity decoding layer. The modelestimates the angular velocity very well with improved spatial frequency independencecompared to the state-of-the-art angular velocity detecting models, when firstly tested by moving sinusoidal gratings. This spatial independence is vital to account forthe honeybee’s flight behaviors. We have also investigated the spatial and temporalresolutions of honeybees to get a bio-plausible parameter setting for explaining thesebehaviors.To investigate whether the model can account for observations of tunnel centeringbehaviors of honeybees, the model has been implemented in a virtual bee simulated bythe game engine Unity. The simulation results of a series of experiments show that theagent can adjust its position to fly through patterned tunnels by balancing the angularvelocities estimated on both eyes under several circumstances. All tunnel stimulationsreproduce similar behaviors of real bees, which indicate that our model does providea possible explanation for estimating the image velocity and can be used for MAV’sflight course regulation in tunnels. What’s more, to further verify the robustness of themodel, the visually guided terrain following simulations have been carried out with aclosed-loop control scheme to restore a preset angular velocity during the flight. Thesimulation results of successfully flying over the undulating terrain verify the feasibility and robustness of the AVDM performing in various application scenarios, whichshows its potential in applications of micro aerial vehicle’s terrain following.In addition, we have also applied the AVDM in grazing landing using only visualinformation. A LGMD neuron is also introduced to avoid collision and to trigger thehover phase, which ensures the safety of landing. By applying honeybee’s landingstrategy of keeping constant angular velocity, we have designed a close-loop controlscheme with an adaptive gain to control landing dynamic using AVDM response asinput. A series of controlled trails have been designed in Unity platform to demonstratethe effectiveness of the proposed model and control scheme for visual landing undervarious conditions. The proposed model could be implemented into real small robotsto investigate the robustness in real landing scenarios in near future.