Many space robotic systems would be required to operate in uncertain or even unknown environments. The problem of identifying such environment 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 and discussed.>

The successful development of fifth-generation systems requires enormous computational capability and flexibility, necessitating the ability to achieve operational responses in hard real-time through optimal resource utilisation and introduction of adaptive control. This necessitates dynamically balancing the computational load among all the processing nodes in the system. In this paper we propose a graph-theoretic, receiver-initiated, distributed protocol for dynamic load balancing protocol in large-scale hypercube ensembles. Using attributed hypergraphs as the primary data structure for constraint modelling and dynamic optimisation, we consider systems running precedence-constrained heterogeneous tasks. Fault Tolerance is ensured by incorporating a dynamic integrity check for the decision nodes and their subsequent re-election if needed. Simulation studies are used to analyse the algorithm performance and correctness.