| With the development of science, kinds of robots have been developed sucessfully. Compared with other type of robot, humanoid robot is easier to accepted by people because its shape and actions are similar with human. Motion planning for humanoid robot is the basic research and also one of the best important problems in the area of humanoid robot. If humanoid robot wants to do kinds of works in the complex environment of human, it need to acquire the different environmental information to do kinds of complex motions, so as to adapt to the human environment and better service to humanity.This thesis is funded by the national 863 key project "sports and entertainment multi-robot systems", and with the purpose of improving the entertainment appreciation and competitive ability of humanoid robot in the competitive entertainment activities, some key technologies of motion planning for humanoid robot are on further research, which includes the stability control, optimal control of falling action, complex motion planning, motion planning in the complex environment and experimental platform of 3 vs 3 humanoid robot soccer game.Firstly, a seven link model of kinematics and dynamics for humanoid robot is built and according to the different motions of robot, models of falling forward and backward actions, climbing up and down the stairs and slopes are present. For humanoid robot, the first problem required to solve is the stability. Some researchers have present kinds of different stability criterions and control methods for the stable control of humanoid robot. This theses introduced a control method based on second-order cone to control the stability of robot and good results are obtained.Secondly, during the process of motion for humanoid robot, it may fall due to some uncertainty factors. Since the falling is inevitable, this theses has done research on the optimal control of falling for humanoid robot. By deeply anlysis of falling action, the kinematics equation of falling is built and optimal control for falling action based on parameter optimum and enhance technology is proposed. For the lack of solving SQP(sequence of quadratic programming) in the parameter optimum method, a new method based on improved SQP filter algorithm is present to improve the optimization process. Good optimization results are obtained by comparing with control of the minimum principleThirdly, since the humanoid robot's shape is similar to people, it is not only walking on the flat but also do some complex motions in environment such as stairs and slopes to adapt the people's living environment really. Researchers has done some researches on the motions for humanoid robot on stairs and slopes. However, humanoid robot is regarded as a multi-degrees and nonlinear complex system, traditional control methods and some intelligent algorithms are not good enough to control its complex motion. In this theses, two methods of nerual network and fuzzy logic control are present to learn offline and control the climbing stairs motion of robot online respectively. To solve the shortcoming of difficult to converge and long time consuming during the trainning process, an improved PSO(Particle Swarm Optimization) optimal algorithm is proposed to train the weights of nerual network and rules of fuzzy logic controller. Experiments show this methods can effectively reduce training time of stairs motion for humanoid robot and get the higher stability.Forthly, for the features of humanoid robot soccer game, this theses present a game control system for 3 vs 3. Master-slave control system is used to control robot based on the task-level. For the basic motions required in the game, fast curve walking based on embedded vision system is designed. Deep research on penalty kick system include shooting and goalkeeping are achieved. The information from environment are gathered by the embedded vision system online, decisions are made through the finite state machine. Experiments show the system can get achieve good control effect.
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