JOURNAL ARTICLE

Biologically inspired visual sensing and flight Control

Geoffrey L. BarrowsJavaan ChahlMandyam V. Srinivasan

Year: 2003 Journal:   The Aeronautical Journal Vol: 107 (1069)Pages: 159-168   Publisher: Cambridge University Press

Abstract

Abstract There is increased interest in new classes of mini- and micro-UAVs with sizes ranging from one metre to ten centimetres. Many envisioned applications of such UAVs require them to be able to fly close to the ground in complex environments. The difficulties associated with flying in such environments coupled with the reduced payload capacity of such airframes means that new methods of sensing and control need to be considered. Good models for such methods are found in the world of flying insects. One particular visual cue used by insects is optic flow, which is the apparent visual motion seen by the insect as a result of its motion through the environment. This paper discusses several research efforts aimed at developing new sensing and control algorithms inspired by insect vision and flight behaviors. These efforts are part of DARPA's controlled biological and biomimetic systems (CBBS) programme. In these (and related) efforts, many elegant control stratagems have been discovered which suggest that simple reflexive schemes combined with the measurement of optic flow may be sufficient to provide many aspects of autonomous navigation in complex environments. Furthermore, these efforts are implementing these behaviors in real flying UAV platforms by using novel hardware and software to measure optic flow, and inserting optic flow measurements into a control loop using a combination of ‘best engineering approaches’ with inspiration taken from biology. This has resulted in fixed and rotary-wing mini-UAVs that are able to hold an altitude and perform terrain following.

Keywords:
Payload (computing) Computer science Terrain Airframe Biomimetics Software Motion control Artificial intelligence Motion (physics) Ranging Control engineering Systems engineering Simulation Aerospace engineering Engineering Robot Ecology Telecommunications

Metrics

65
Cited By
4.71
FWCI (Field Weighted Citation Impact)
18
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Biomimetic flight and propulsion mechanisms
Physical Sciences →  Engineering →  Aerospace Engineering
Smart Agriculture and AI
Life Sciences →  Agricultural and Biological Sciences →  Plant Science

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