 A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common RoutesOlivier J N Bertrand, Jens P Lindemann and Martin Egelhaaf PLOS Computational Biology, 2015, vol. 11, issue 11, 1-28 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.Author Summary: The number of robots in our surroundings is increasing continually. They are used to rescue humans, inspect hazardous terrain or clean our homes. Over the past few decades, they have become more autonomous, safer and cheaper to build. Every autonomous robot needs to navigate in sometimes complex environments without colliding with obstacles along its route. Nowadays, they mostly use active sensors, which induce relatively high energetic costs, to solve this task. Flying insects, however, are able to solve this task by mainly relying on vision. Any agent, both biological and technical, experiences an apparent motion of the environment on the retina, when moving through the environment. The apparent motion contains entangled information of self-motion and of the distance of the agent to objects in the environment. The later is essential for collision avoidance. Extracting the relative distance to objects from geometrical apparent motion is a relatively simple task. However, trying to accomplish this with biological movement detectors, i.e. movement detectors found in the animal kingdom, is tricky, because they do not provide unambiguous velocity information, but are much affected also by the textural properties of the environment. Inspired by the abilities of insects, we developed a parsimonious algorithm to avoid collisions in challenging environments solely based on elementary motion detectors. We coupled our algorithm to a goal direction and then tested it in cluttered environments. The trajectories resulting from this algorithm show interesting goal-directed behavior, such as the formation of a small number of routes, also observed in navigating insects. Date: 2015 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004339 DOI: 10.1371/journal.pcbi.1004339 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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