Goal-Directed Navigation of an Autonomous Flying Robot Using Biologically Inspired Cheap Vision

In nature, flying insects are capable of surprisingly good navigation, despite the small size and relative simplicity of their brains. Recent experimental research in biology has uncovered a number of different ways in which insects use cues derived from optical flow for navigational purposes, such as obstacle avoidance, safe landing and dead-reckoning. Inspired by the visual navigation of flying insects, this paper presents a model of vision-based navigation using Elementary Motion Detectors (EMDs). The performance tests with an autonomous flying robot successfully demonstrate goal-directed navigation in an unstructured environment, as well as obstacle avoidance and course stabilization behaviors. Further investigation in the simulation shows that goal-directed navigation can be potentially achieved by simple visual processing, and that the design flexibility of this approach leads to high adaptivity to the given task-environment.

[1]  Martin Egelhaaf,et al.  INTRINSIC PROPERTIES OF BIOLOGICAL MOTION DETECTORS PREVENT THE OPTOMOTOR CONTROL SYSTEM FROM GETTING UNSTABLE , 1996 .

[2]  M. Srinivasan,et al.  Reflective surfaces for panoramic imaging. , 1997, Applied optics.

[3]  Heinrich H. Bülthoff,et al.  On robots and flies: Modeling the visual orientation behavior of flies , 1999, Robotics Auton. Syst..

[4]  M V Srinivasan,et al.  Honeybee navigation: nature and calibration of the "odometer". , 2000, Science.

[5]  Mandyam V. Srinivasan,et al.  Motion detection in insect orientation and navigation , 1999, Vision Research.

[6]  Christof Koch,et al.  A Neuromorphic Visual Motion Sensor For Real-World Robots , 1998 .

[7]  Fumiya Iida,et al.  Navigation in an autonomous flying robot by using a biologically inspired visual odometer , 2000, SPIE Optics East.

[8]  Nicolas H. Franceschini,et al.  Towards UAV Nap-of-the-Earth Flight Using Optical Flow , 1999, ECAL.

[9]  W. Reichardt.  Movement perception in insects , 1969 .

[10]  Zhang,et al.  Visually mediated odometry in honeybees , 1997, The Journal of experimental biology.

[11]  George A. Bekey,et al.  The USC autonomous flying vehicle: an experiment in real-time behavior-based control , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[12]  Fabrizio Mura,et al.  Visual control of altitude and speed in a flying agent , 1994 .

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

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

[15]  Heinrich H. Bülthoff,et al.  Modeling Obstacle Avoidance Behavior of Files Using an Adaptive Autonomous Agent , 1997, ICANN.

[16]  Heinrich H. Bülthoff,et al.  Minimalistic Approach to 3D Obstacle Avoidance Behavior from Simulated Evolution , 1997, ICANN.

[17]  Dimitrios Lambrinos,et al.  Cheap Vision-Exploiting Ecological Niche and Morphology , 2000, SOFSEM.

[18]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[19]  Simon Fürst,et al.  A vision based navigation system for autonomous aircraft , 1999, Robotics Auton. Syst..

[20]  Omead Amidi,et al.  3-D Site Mapping with the CMU Autonomous Helicopter , 1998 .

[21]  Svetha Venkatesh,et al.  How honeybees make grazing landings on flat surfaces , 2000, Biological Cybernetics.