Toward an insect-inspired event-based autopilot combining both visual and control events

Abstract

This paper presents the autopilot and the behavior of a "simulated bee" traveling along two different tunnels using both visual and control events. The computational gain of an event-based PID controller compared to its time-based version is usually discussed because the event detector is computationally expensive. By combining visual and control events, the newly suggested event-based autopilot requires very low computational resources. In particular, the event detector which computes the control error and tests its magnitude is activated only when a new contrast is detected by the optic motion detectors that assesses the optic flow, i.e. only when the magnitude of the optic flow error could have changed. This new event-based control strategy used faithfully the visual information already available in the optic flow sensor to reduce even further the computational cost. The "simulated bee" was equipped with: (i) a minimalistic compound eye comprising 10 or 8 local motion sensors (depending of the tunnel configuration) measuring the optic flow magnitude, (ii) two optic flow regulators updating the control signals whenever specific optic flow criteria changed and (iii) three event-based controllers taking into account both error signals and visual events, each one in charge of its own translational dynamics. The "simulated bee" managed to travel safely along the tunnels without requiring any speed or distance measurements, using very low computational resources, by (i) concomitantly adjusting the side thrust, vertical lift and forward thrust only when both a visual contrast and a change of optic flow control error were detected, and (ii) avoiding collisions with the surface of the tunnels and decreasing or increasing its speed, depending on the clutter rate perceived by motion sensors.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

“…More concretely, there are many ground and flying robots benefiting from the OF-based sensors which are used for guiding the robots for autopilots, e.g. [187,183,161,62,63] , and collision avoidance ( Fig. 25a, 25c), e.g.…”

“…[193,184,141,53] and tunnel crossing or travelling, e.g. [14,161] and corridor centring response, e.g. [5,41,192] and collision or obstacle avoidance, e.g.…”

Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness

“…Additional analysis of the insect visuomotor system shows how specialized tangential cells process distributed optic flow measurements, inspiring the wide-field integration framework presented in [ 32 , 33 , 34 ]. Through observation of insects and other flying animals in different environments, optic flow-based navigation methods were developed [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Small-obstacle avoidance algorithms based on optic flow measurements are demonstrated onboard a multirotor platform in [ 43 , 44 ].…”

Floor and ceiling mirror configurations to study altitude control in honeybees

“…The minimum sized section of a rectangular tunnel sets the honeybees' forward speed [5,7]; therefore, the forward and upward axes of the honeybee's flight control system can be decoupled [2]. Consequently, in a tunnel where the width is smaller than the height, the width sets the honeybees' forward speed (see also [8][9][10][11] for a description of the visuomotor modelling in honeybees). Similarly, honeybees, which have be trained to follow the tunnel ceiling, when encountering a 'dorsal ditch' in the middle of the tunnel configuration [4] responded to this new configuration by rising quickly and hugging the new, higher ceiling, while maintaining a similar forward speed, distance to the ceiling, and dorsal optic flow to those observed during the training step.…”