Many space robotic systems would be required to operate in uncertain or even unknown environments. In the paper, the problem of identifying such environments for compliance control is considered. In particular, neural networks are used for identifying environments that a robot establishes contact with. Both function approximation and parameter identification (with fixed nonlinear structure and unknown parameters) results are presented. The environment model structure considered is relevant to two space applications: cooperative execution of tasks by robots and astronauts, and sample acquisition during planetary exploration. Compliant motion experiments have been performed with a robotic arm, placed in contact with a single degree-of-freedom electromechanical environment. In the experiments, desired contact forces are computed using a neural network, given a desired motion trajectory. Results of the control experiments performed on robot hardware are described, along with relevant discussions.
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