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Research made over the past decade shows the use of increasingly complex methods and heavy platforms to achieve autonomous flight in cluttered environments. However, efficient behaviors can be found in nature where limited sensing is used, such as in insects progressing toward a light at night. Interestingly, their success is based on their ability to recover from the numerous collisions happening along their imperfect flight path. The goal of the AirBurr project is to take inspiration from these insects and develop a new class of flying robots that can recover from collisions and even exploit them. Such robots are designed to be robust to crashes and can take-off again without human intervention. They navigate in a reactive way and, unlike conventional approaches, they don’t need heavy modelling in order to fly autonomously. We believe that this new paradigm will bring flying robots out of the laboratory environment and allow them to tackle unstructured, cluttered environments. This paper aims at presenting the vision of the AirBurr project, as well as the latest results in the design of a platform capable of sustaining collisions and self-recovering after crashes.
Posted on: March 7, 2012
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Tweet
Posted on: September 25, 2019
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Vision Tape is a novel class of flexible compound-eye-like linear vision sensor dedicated to motion extraction and proximity estimation. This novel sensor possesses intrinsic mechanical flexibility that provides wide-range adaptive shape, allowing adjustable field of view as well as integration with numerous substrates and curvatures. Vision Tape extracts Optic Flow of the visual scene to calculate the motion vector, which allows proximity estimation based on the motion parallax principle.
Posted on: August 31, 2011
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Tweet
Posted on: September 10, 2018