Abstract
We have deployed an anatomically constrained neural model for path integration on a real world, holonomic aerial platform. Based on the insect central complex, the model combines estimated heading and ground speed information to maintain a location estimate that can be used to steer the agent directly home after convoluted outward journeys. We implement a biologically plausible method to estimate ground speed using optical flow. We discover that a downward viewing, mechanically stabilised and height compensated vision system performs well in a range of natural environments, even when visual acuity is reduced to \(3^{\circ }\)/pixel. In a flat outdoor environment, the worst case final displacement error increases at a rate 1.5 m 100 m outbound travel. The field of view of the vision system has no impact on odometry performance.
Supported by the Edinburgh Centre for Robotics and the Engineering and Physical Sciences Research Council. We thank Stanley Heinze who provided the neural data and the initial illustration for Fig. 3. Thanks also to Jiale Lu for his initial work.
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Stankiewicz, J., Webb, B. (2020). Using the Neural Circuit of the Insect Central Complex for Path Integration on a Micro Aerial Vehicle. In: Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T.J., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science(), vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_31
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