The premise of analyzing and researching robot technology is to establish a proper mathematical model and then to solve it with kinematics. In this study, a self-developed humanoid hydraulically driven dual-arm robot is taken as the research object, and the DH (Denavit–Hartenberg) parameter method and the rotational exponential formula (POE) are used to solve the kinematics of the robot. The calculation results are verified by simulation. The advantages and disadvantages of the two methods are analyzed. The differences between the two methods are compared. It lays a foundation for other scholars to choose mathematical models when analyzing the mechanism in the future.

A multi-objective trajectory planning method for redundantly actuated parallel manipulator under hybrid force and position control is proposed considering the coordination of its driving forces. Combining inverse kinematics analysis, the driving force coordination of the manipulator is analyzed using the virtual work principle. On this basis, the B-spline curve is used to plan the motion trajectory of the end-effector of the manipulator in the Cartesian space. Subsequently, the driving force coordination, total motion time, and maximum absolute value of the jerk impulse of the actuator are defined as objective functions, and a multi-objective optimization problem is solved using the multi-objective particle swarm algorithm (MOPSO). Finally, the potential optimal solution is selected by constructing a comprehensive optimal evaluation function. The trajectory simulation results verify that the trajectory planning method is effective and universal.

When the two arms of the robot are transporting the heavy loads together, a new parallel mechanism is formed. The actuator input selection and optimization of the parallel mechanism are basic and important problems in mechanism research. In this paper, a 2-RPPPS dual-arm robot is taken as the research object. Firstly, based on the screw theory and input selection principle, 158 reasonable schemes are obtained. Then, an evaluation mechanism is established to screen out the schemes that do not conform to the input selection principle. Then, the end effector of the parallel mechanism moves along two different trajectories. Using the particle swarm optimization algorithm, the inverse kinematics solution of each trajectory is obtained, and the velocity and acceleration of each actuator under different trajectories are obtained. Finally, the motion stability of each actuator is evaluated, and the best scheme is selected. The results show that the best input scheme can be selected according to different trajectories, so as to improve the performance of the parallel mechanism. To the authors' knowledge, no one has done any research on selecting the appropriate input scheme according to the trajectory of the end effector.