A FPGA realisation structure based on CORDIC is proposed to implement inverse kinematics for a biped robot. First, the angle equations of inverse kinematics for the biped robot are described. Based on these equations represented by the inverse tangent function, a FPGA structure based on two CORDIC operators of circular vectoring and hyperbolic vectoring is proposed and implemented on a FPGA chip. Finally, comparison of the proposed FPGA realisation and the software realisation to calculate these angle equations is presented to illustrate the effectiveness of the proposed structure.

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In this paper, a motion planning method based on the Soft Actor-Critic (SAC) is designed for a dual-arm robot with two 7-Degree-of-Freedom (7-DOF) arms so that the robot can effectively avoid selfcollision and at the same time can avoid the joint limits and singularities of the arm. The left-arm and rightarm of the dual-arm robot each have a neural network to control its position and orientation. Dual-agent training, distributed training structure, and progressive training environment are used to train neural networks. During the training process, the motion of one arm is regarded as the environment of the other arm, and the two agents are trained at the same time. In the input part of the neural network of the proposed method, all parameters come from the angle of each axis and kinematic calculation, no additional sensors are needed, so the method is easier to transplant to different dual-arm robots. With some appropriate neural network inputs and reward functions design, the robot can perform the expected self-collision avoidance and effectively avoid the joint limits and singularities of the arm. Finally, some experiments of the simulation tests in the Gazebo simulator and actual tests in a laboratory-made dual-arm robot are presented to illustrate the proposed SAC-based motion planning method is feasible and practicable in the avoidance of self-collision, joint limits, and singularities.

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