Improvement of robot control by neural computers

Currently available robot control systems have various limitations in comparison to biological motor systems partly due to a lack of a general control theory for robots in a dynamic environment and partly due to the real-time challenge for conventional computers. The authors review current approaches to (1) speeding up the neural computation by means of adaptive load distribution on massively parallel computers and (2) the design of adaptive neural net modules for path planning, obstacle avoidance, inverse kinematics, system identification, and dynamic control of robot manipulators.<
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