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This manuscript addresses the problem of obstacle avoidance for semi- and autonomous terrestrial platforms in dynamic and unknown environments. Based on monocular vision, it proposes a set of tools that continuously monitors the way forward, proving appropriate road information in real time. Taking into account the temporal coherence between consecutive frames, a new Dynamic Power Management methodology is proposed and applied to a robotic visual machine perception, which included a new environment observer method to optimize energy consumption used by a visual machine. A remarkable…mehr

Produktbeschreibung
This manuscript addresses the problem of obstacle avoidance for semi- and autonomous terrestrial platforms in dynamic and unknown environments. Based on monocular vision, it proposes a set of tools that continuously monitors the way forward, proving appropriate road information in real time. Taking into account the temporal coherence between consecutive frames, a new Dynamic Power Management methodology is proposed and applied to a robotic visual machine perception, which included a new environment observer method to optimize energy consumption used by a visual machine. A remarkable characteristic of these methodologies is its independence of the image acquiring system and of the robot itself. This real-time perception system has been evaluated from different test-banks and also from real data obtained by two intelligent platforms. In semi-autonomous tasks, tests were conducted at speeds above 100 Km/h. Autonomous displacements were also carried out successfully.
Autorenporträt
He obtained his Ph.D. degree in systems and information technologies from the Université de Technologie de Compiègne (UTC) and in mechanical engineering from the Universidade Estadual de Campinas (UNICAMP) in 2011. His research interests are Intelligent Vehicles and Autonomous Vehicles.