The purpose of this research is to generate natural motion of the biped locomotion robot like a human walking in various environments. In this paper, we report a method of natural motion generation of a biped locomotion robot. We apply the hierarchical trajectory generation method to generate the trajectory of a biped locomotion robot through energy optimization and aim to generate more natural motion by considering dynamic effect. The hierarchical trajectory generation method consists of two layers, one is the GA layer which minimizes the total energy of all actuators, and the other is the evolutionary programming (EP) layer which optimizes interpolated configuration of a biped locomotion robot. Then we formulate the trajectory generation problem, as a minimizing problem of energy and apply the hierarchical trajectory generation method. Further, we build a biped locomotion robot in trial, which has 13 joints and made of aluminium materials. Finally, we confirm that the calculated natural motion trajectory can be applied successfully to the practical biped locomotion.

The purpose of this research is to generate natural motion of the biped locomotion robot such as the walking of a human in various environments. In this paper, we propose a method of stable motion generation of a biped locomotion robot. We apply the control of this proposed method with eight force sensors at the soles of the biped locomotion robot. The zero moment point (ZMP) is a well known index of stability in walking robots. ZMP is determined by the configuration of the robots. However, there are many configurations against the ZMP. Because of that, when we use ZMP as the stabilization index, we must select the best configuration in many stability configurations. Then it is a problem of which configurations are selected. In this paper, we solve the problem with recurrent neural networks. In both the single support and double support periods, we calculate the position of ZMP by using values from four force sensors at each sole, and actuation joints and the angles can be determined by recurrent neural networks without ZMP moving out from the supporting area of sole. We employ the recurrent neural networks with genetic algorithms for learning capability and self-adaptive mutation operator. Further, we build a biped locomotion robot in trial, which has 13 joints and verified that the calculated stable motion trajectory can be successfully applied to the practical biped locomotion.

This paper deals with an application of a virus-evolutionary genetic algorithm (VEGA) to hierarchical trajectory planning of a redundant manipulator. The hierarchical trajectory planning is composed of a trajectory generator and position generator. The position generator generates collision-free intermediate positions of the redundant manipulator. The trajectory generator generates a collision-free trajectory based on some intermediate positions sent from the position generator. To generate a collision-free trajectory of the redundant manipulator, the VEGA is applied to the hierarchical trajectory planning only based on forward kinematics. The VEGA realizes horizontal propagation and vertical inheritance of genetic information in a population of candidate solutions. The main operator of the VEGA is a reverse transcription operator, which plays the roles of a crossover and selection simultaneously. In this paper, self-adaptive mutation is applied to the VEGA for local search of trajectory planning to obtain higher performance and the quick solution. Simulation results of the hierarchical trajectory planning show that the VEGA can generate a collision-free trajectory.