Sensors for mobile robots: theory and application
Sensors for mobile robots: theory and application
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor Modelling, Design and Data Processing for Autonomous Navigation in Confined Environments
Sensor Modelling, Design and Data Processing for Autonomous Navigation in Confined Environments
A Silicon Implementation of the Fly's Optomotor Control System
Neural Computation
Visual motion pattern extraction and fusion for collision detection in complex dynamic scenes
Computer Vision and Image Understanding
Research advances in intelligent collision avoidance and adaptive cruise control
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
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Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using `randomised winner-take-all' or `steering wheel' algorithms for LGMD model integration, the Khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research.