From the point of view of set theory and mathematics, the relation between the forward kinematics (FK) and the inverse kinematics (IK) can be regarded as a nonlinear mapping between the joint space and the operation space of the robot manipulator. Considering the powerful ability of the artificial neural networks (ANN) to process nonlinear mapping relations, the IK problem can be transformed into the problem of training the weights of ANN. In this work, the solution of the IK of the MOTOMAN manipulator is implemented by using ANN. Because of its local approach ability, the radial basis function (RBF) networks of six inputs and one output are designed. The method avoids the traditional complicated deriving equations procedure and programming. Examples are given to illustrate that RBF networks not only have better computation precision than back propagation (BP) networks but also converge faster than BP networks.

In this paper a method for the automatic generation of dynamics for modular robots is presented. A modular reconfigurable robot consists of link modules and joint modules of various specifications. We analyze the abstract architecture of modular robots. A total of nine types of connecting forms and three types of joint forms are identified with reference to their own systems. The geometric relationships are derived by the group theory. According to the modular idea, the formulations of the velocity, acceleration and other essential equations of the link module and the joint module are formed compensatively and recursively. Then the dynamic model is generated automatically by a compensative and recursive method.

The paper presents research on the APF approach for solving the GNRON and local minima problems. The repulsive potential function is modified in order to solve the GNRON problem. A simulated annealing algorithm integrated into the APF has solved the local minimum problem. The improved APF is applied to the path-planning problem of soccer robots. The simulated experiments show the validity of this approach.

No abstract