Honeybees as a Model for the Study of Visually Guided Flight, Navigation, and Biologically Inspired Robotics

Abstract

Research over the past century has revealed the impressive capacities of the honeybee, Apis mellifera , in relation to visual perception, flight guidance, navigation, and learning and memory. These observations, coupled with the relative ease with which these creatures can be trained, and the relative simplicity of their nervous systems, have made honeybees an attractive model in which to pursue general principles of sensorimotor function in a variety of contexts, many of which pertain not just to honeybees, but several other animal species, including humans. This review begins by describing the principles of visual guidance that underlie perception of the world in three dimensions, obstacle avoidance, control of flight speed, and orchestrating smooth landings. We then consider how navigation over long distances is accomplished, with particular reference to how bees use information from the celestial compass to determine their flight bearing, and information from the movement of the environment in their eyes to gauge how far they have flown. Finally, we illustrate how some of the principles gleaned from these studies are now being used to design novel, biologically inspired algorithms for the guidance of unmanned aerial vehicles.

Biomimetic Autopilot Based on Minimalistic Motion Vision for Navigating along Corridors Comprising U-shaped and S-shaped Turns

“…Neither the texture on the walls nor the phototransduction processes involved were taken into account in the simulations in order to save computational resources because bees' behaviors (centering response [1] and bees' speed control [20]) were not texture-or contrast-dependent. In addition, the authors of electrophysiological studies on honeybees' motion sensitive neurons, which have been called velocity-tuned neurons (VT neurons), have established that their responses were closely correlated with the motion of the patterns [6].…”

“…The forward speed profile in figure 5c shows that the robot's speed is either adjusted to ensure environmental clearance or held constant at a straight junction, or even slowed down drastically during saccades. Since the frontolateral ±30° facets detect the lateral OF before the lateral ±90° facets, the robot can be said to be endowed with a kind of prediction horizon [18] that helps it to reject lateral disturbances in a highly converging section, as observed to occur in insects flying along corridors with a variable width [1,21,23,27].…”

“…Flying insects have been found to rely mainly on the optic flow (OF) [1,2], which can be defined as the ratio between their relative speed and their distance from obstacles in the surrounding environment [3]. Specialized neurons called lobula plate tangential cells (LPTCs) seem to serve as wide-field OF sensors in head stabilization and visual guidance processes (Flies: [4,5];…”

“…15 in [19]). This lateral OF regulation principle may explain how flying insects are able to control their speed depending on the narrowness of the environment [20][21][22][23] while avoiding walls [1,24] when travelling along straight and tapered corridors, keeping a straight course.…”

“…The so-called 'OF balance' hypothesis initially put forward to explain bees' centering behavior [1] has been implemented in a large number of visually guided wheeled robots [28][29][30][31] travelling along various corridors, including even some with bends [29,30] and L-junctions [30,31], but none of these robots were able to cope with U-turns or S-shaped turns, as far as we know. Other collision-avoidance algorithms based on the OF have been implemented onboard microflyers [32][33][34] using a series of open-loop commands to trigger saccades to avoid frontal obstacles.…”