7 research outputs found

    Embodied Models and Neurorobotics

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    Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience

    Bio-Inspired Robotics

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    Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

    Disturbance rejection for U.A.S. aircraft using bio-inspired strain sensing

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    A bio inspired gust rejection mechanism based on structural inputs is proposed. Insect wings possess a wealth of sensor systems which typically consist of fast reflexive neuronal paths. Stretch and strain sensors on insect wings are used for flight control and can be found across many species. These are used for monitoring of bending and torsion during flight. The fast reflexive and proprioceptive mechanisms based on strain sensing found in nature are the inspiration for this work. A strain feedback controller allows for anticipation of the onset of rigid body dynamics due to gust perturbations. This anticipation stems from sensing of higher order states and the possibility of reacting before lower order states are reached. High bandwidth inner loop compensation is therefore enabled. Forces and moments are proportional to wing strain patterns and can be used in fast reaction inner loops. Strain sensors are used for providing an indirect estimation of the differential forces applied to the aircraft wing and therefore to the aircraft rigid body. These sensors can be distributed over the surface of the aircraft wing to encode multiple degree of freedom disturbances. Sensor locations for disturbance rejection are determined based on metrics associated to the observability Grammian. The locations are preselected based on modal energy analyses and are chosen according to wide field integration patterns. A model for wide field integrated strain based on mass participation factors is proposed as well as one which is based on the physics of the forces and moments acting on the wing producing strain patterns which can be used for disturbance rejection. Models of the differential forces via strains on the wings are proposed. Strain feedback was implemented in four platforms under different types of disturbances. The platforms consisted of a glider, a quadrotor, a wing section for wind tunnel testing and an RC airplane with a full span wing. The disturbances included discrete gusts as well as turbulence. The results of using strain feedback showed not only to be faster than IMU estimations but also to be better when compared to a classical attitude controller implementation
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