Robot Navigation Inspired by Principles of Insect Vision

Srinivasan, M. V.; Chahl, J.S.; Weber, K.; Venkatesh, Svetha; Nagle, M. G.; Zhang, S.W.

doi:10.1007/978-1-4471-1273-0_3

Cited by 33 publications

(26 citation statements)

References 19 publications

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“…Indeed, insects like bees and flies have evolved alternative, simple and ingenious stratagems for dealing with the problem of 3D vision to perform navigational tasks. These behaviors originated in research on insect flight are appropriate for implementation in a biomimetic autopilot for small UAVs and robotics in general [19][20][21]. Potential applications of optical flow for small aerial vehicles include altitude control and terrain following [22,23], autonomous landing [22,24,25] and obstacles avoidance [26][27][28].…”

Section: Bio-inspired Vision-based Aerial Navigationmentioning

confidence: 98%

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

Kendoul¹,

Fantoni²,

Nonami³

2009

Robotics and Autonomous Systems

228

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a b s t r a c tThe problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow.

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Section: Bio-inspired Vision-based Aerial Navigationmentioning

confidence: 98%

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

Kendoul¹,

Fantoni²,

Nonami³

2009

Robotics and Autonomous Systems

228

View full text Add to dashboard Cite

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“…Bees also exhibit a clutter response that allows their speed to be adapted to the width of the tunnel by maintaining a constant average motion [38]. Srinivasan et al implemented the corresponding strategies on a wheeled robot to demonstrate their efficiency [36]. Likewise, Franceschini et al [18] described how the organization of the compound eye of the housefly, and how the neural processing of visual information obtained during the flight, allow this insect to compute its distances to lateral obstacles and to avoid them.…”

Section: Biological Inspirationmentioning

confidence: 99%

A contribution to vision-based autonomous helicopter flight in urban environments

Muratet

Doncieux

Brière³

et al. 2005

Robotics and Autonomous Systems

102

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A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV's survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms.

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“…Vision has been widely used to enlarge the perception capabilities of robots (Adorni et al, 2001;Huber and Kortenkamp, 1998;Paulino et al, 2001;Srinivasan et al, 1999;Wells et al, 1996). The use of 3D models has become very popular nowadays (Gasteratos et al, 2002;Stevens and Beveridge, 2000;Zheng et al, 1993).…”

Section: Introductionmentioning

confidence: 97%

Detection of doors using a genetic visual fuzzy system for mobile robots

2006

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Doors are common objects in indoor environments and their detection can be used in robotic tasks such as map-building, navigation and positioning. This work presents a new approach to door-detection in indoor environments using computer vision. Doors are found in gray-level images by detecting the borders of their architraves. A variation of the Hough Transform is used in order to extract the segments in the image after applying the Canny edge detector. Features like length, direction, or distance between segments are used by a fuzzy system to analyze whether the relationship between them reveals the existence of doors. The system has been designed to detect rectangular doors typical of many indoor environments by the use of expert knowledge. Besides, a tuning mechanism based on a genetic algorithm is proposed to improve the performance of the system according to the particularities of the environment in which it is going to be employed. A large database of images containing doors of our building, seen from different angles and distances, has been created to test the performance of the system before and after the tuning process. The system has shown the ability to detect rectangular doors under heavy perspective deformations and it is fast enough to be used for real-time applications in a mobile robot.

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Robot Navigation Inspired by Principles of Insect Vision

Cited by 33 publications

References 19 publications

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

A contribution to vision-based autonomous helicopter flight in urban environments

Detection of doors using a genetic visual fuzzy system for mobile robots

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