  A neural network system in an animal brain contains many modules and
generates adaptive behavior by integrating the outputs from the modules. The
mathematical modeling of such large systems to elucidate the mechanism of
rapidly finding solutions is vital to develop control methods for robotics and
distributed computation algorithms. In this article, we present a network model
to solve kinematics and dynamics problems for robot arm manipulation. This
model represents the solution as an attractor in the phase space and also finds
a new solution automatically when perturbations such as variations in the end
position of the arm or obstacles occur. In the proposed model, the physical
constraints, target position, and the existence of obstacles are represented by
network connections. Therefore, the theoretical framework of the model remains
almost the same when the number of constraints increases. In addition, as the
model is regarded as a distributed system, it can be applied toward the
development of parallel computation algorithms.
