ArticlePLoS computational biology2015
A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 56 citations in OpenAlex.
- Navigating in clutter: how bumblebees optimize flight behaviour through experience.The Journal of experimental biology · 2025Article
- Path integration and optic flow in flying insects: a review of current evidence.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2025Review
- A Computationally Efficient Neuronal Model for Collision Detection with Contrast Polarity-Specific Feed-Forward Inhibition.Biomimetics (Basel, Switzerland) · 2024Article
- Analysis of collision avoidance in honeybee flight.Journal of the Royal Society, Interface · 2024Article
- Finding the gap: neuromorphic motion-vision in dense environments.Nature communications · 2024Article
- Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review.Biomimetics (Basel, Switzerland) · 2023Review
- Optic flow based spatial vision in insects.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2023Review
- The potential underlying mechanisms during learning flights.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2023Review
- Article
- Contrast independent biologically inspired translational optic flow estimation.Biological cybernetics · 2022Article
- Bumblebees display characteristics of active vision during robust obstacle avoidance flight.The Journal of experimental biology · 2022Article
- Learning from animals: How to Navigate Complex Terrains.PLoS computational biology · 2020Article
- From Paths to Routes: A Method for Path Classification.Frontiers in behavioral neuroscience · 2020Article
- The Critical Role of Head Movements for Spatial Representation During Bumblebees Learning Flight.Frontiers in behavioral neuroscience · 2020Article
- Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR.PloS one · 2020Article
- Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework.Frontiers in neurorobotics · 2020Article
- The role of optic flow pooling in insect flight control in cluttered environments.Scientific reports · 2019Article
- Spatial Encoding of Translational Optic Flow in Planar Scenes by Elementary Motion Detector Arrays.Scientific reports · 2018Article
- The problem of home choice in skyline-based homing.PloS one · 2018Article
- Local motion adaptation enhances the representation of spatial structure at EMD arrays.PLoS computational biology · 2017Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.