Insect inspired behaviours for the autonomous control of mobile robots

Animals navigate through various uncontrolled environments with seemingly little effort. Flying insects, especially, are quite adept at manoeuvring in complex, unpredictable and possibly hostile environments. Through both simulation and real-world experiments, we demonstrate the feasibility of equipping a mobile robot with the ability to navigate a corridor environment, in real time, using principles based on insect-based visual guidance. In particular we have used the bees' navigational strategy of measuring object range in terms of image velocity. We have also shown the viability and usefulness of various other insect behaviours: 1) keeping walls equidistant, 2) slowing down when approaching an object, 3) regulating speed according to tunnel width, and 4) using visual motion as a measure of the distance travelled.

[1]  Esch,et al.  Distance estimation by foraging honeybees , 1996, The Journal of experimental biology.

[2]  George Adrian Horridge,et al.  A theory of insect vision: velocity parallax , 1986, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  Eka Swadiansa.  The hypothesis , 1990 .

[4]  H. Wagner.  Flow-field variables trigger landing in flies , 1982, Nature.

[5]  M V Srinivasan,et al.  How insects infer range from visual motion. , 1993, Reviews of oculomotor research.

[6]  G. K. Wallace.  Visual Scanning in the Desert Locust Schistocerca Gregaria Forskål , 1959 .

[7]  Mandyam V. Srinivasan,et al.  Structure from motion: determining the range and orientation of surfaces by image interpolation , 1996 .

[8]  R. Hengstenberg.  Multisensory control in insect oculomotor systems. , 1993, Reviews of oculomotor research.

[9]  F. A. Miles,et al.  Visual Motion and Its Role in the Stabilization of Gaze , 1992 .

[10]  Karen Roberts,et al.  Centering behavior using peripheral vision , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[11]  M. Heisenberg,et al.  The sensory-motor link in motion-dependent flight control of flies. , 1993, Reviews of oculomotor research.

[12]  Giulio Sandini,et al.  Divergent stereo for robot navigation: learning from bees , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Svetha Venkatesh,et al.  Insect based navigation and its applications to the autonomous control of mobile robots , 1994 .

[14]  Zhang,et al.  Honeybee navigation en route to the goal: visual flight control and odometry , 1996, The Journal of experimental biology.

[15]  Martin Herman,et al.  Real-time obstacle avoidance using central flow divergence and peripheral flow , 2017, Proceedings of IEEE International Conference on Computer Vision.

[16]  William H. Warren,et al.  Robot navigation from a Gibsonian viewpoint , 1994, Proceedings of IEEE International Conference on Systems, Man and Cybernetics.

[17]  M. Srinivasan,et al.  Range perception through apparent image speed in freely flying honeybees , 1991, Visual Neuroscience.

[18]  K. Nakayama,et al.  Optical Velocity Patterns, Velocity-Sensitive Neurons, and Space Perception: A Hypothesis , 1974, Perception.