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A neural network system in an animal brain contains many modules andgenerates adaptive behavior by integrating the outputs from the modules. Themathematical modeling of such large systems to elucidate the mechanism ofrapidly finding solutions is vital to develop control methods for robotics anddistributed computation algorithms. In this article, we present a network modelto solve kinematics and dynamics problems for robot arm manipulation. Thismodel represents the solution as an attractor in the phase space and also findsa new solution automatically when perturbations such as variations in the endposition of the arm or obstacles occur. In the proposed model, the physicalconstraints, target position, and the existence of obstacles are represented bynetwork connections. Therefore, the theoretical framework of the model remainsalmost the same when the number of constraints increases. In addition, as themodel is regarded as a distributed system, it can be applied toward thedevelopment of parallel computation algorithms.

Application of autonomous pathfinding system to kinematics and dynamics problems by implementing network constraints