Abstract
Avoiding collisions is one of the most basic needs of any mobile agent, both biological and technical, when searching around or aiming toward a goal. We propose a model of collision avoidance inspired by behavioral experiments on insects and by properties of optic flow on a spherical eye experienced during translation, and test the interaction of this model with goal-driven behavior. Insects, such as flies and bees, actively separate the rotational and translational optic flow components via behavior, i.e. by employing a saccadic strategy of flight and gaze control. Optic flow experienced during translation, i.e. during intersaccadic phases, contains information on the depth-structure of the environment, but this information is entangled with that on self-motion. Here, we propose a simple model to extract the depth structure from translational optic flow by using local properties of a spherical eye. On this basis, a motion direction of the agent is computed that ensures collision avoidance. Flying insects are thought to measure optic flow by correlation-type elementary motion detectors. Their responses depend, in addition to velocity, on the texture and contrast of objects and, thus, do not measure the velocity of objects veridically. Therefore, we initially used geometrically determined optic flow as input to a collision avoidance algorithm to show that depth information inferred from optic flow is sufficient to account for collision avoidance under closed-loop conditions. Then, the collision avoidance algorithm was tested with bio-inspired correlation-type elementary motion detectors in its input. Even then, the algorithm led successfully to collision avoidance and, in addition, replicated the characteristics of collision avoidance behavior of insects. Finally, the collision avoidance algorithm was combined with a goal direction and tested in cluttered environments. The simulated agent then showed goal-directed behavior reminiscent of components of the navigation behavior of insects.
Highlights
Anyone who has tried to catch flying flies will be familiar with their amazing performance
Since the relative nearness is linked to the optic flow, the first step of the collision avoidance algorithm is to transform the optic flow into relative nearness
The translational optic flow, i.e. the optic flow experienced during the brief translatory phases of self-motion modeled after the intersaccadic intervals of insect flight, is determined jointly by the agent’s self-motion and the three-dimensional structure of the environment
Summary
Anyone who has tried to catch flying flies will be familiar with their amazing performance. Within a fraction of a second, flies perform high-speed turns to avoid a predator or a collision with an obstacle. The optic flow experienced during translations in a static environment depends on the agent’s speed, its nearness to objects and its motion direction. When the agent moves fast or close to objects, the optic flow amplitude will be high. The rotational optic flow depends only on the ego-motion of the agent and, is independent of the spatial layout of the environment. The saccade amplitude of an insect or a bird is thought to be driven, at least in the vicinity of potential obstacles, by the optic flow gathered during the translation preceding the saccade
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