AUTHOR=Zhao Guojun , Jiang Du , Liu Xin , Tong Xiliang , Sun Ying , Tao Bo , Kong Jianyi , Yun Juntong , Liu Ying , Fang Zifan 

TITLE=A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.832829

DOI=10.3389/fbioe.2022.832829

ISSN=2296-4185

ABSTRACT=The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot's positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. At the same time, this paper models the positive kinematics of the general six-degree-of-freedom industrial robotic arm in the form of exponential product (POEs) based on the spinor theory, and proves the high accuracy of the algorithm by setting multiple sets of joint angles and performing verification. Finally, this paper compares the algorithm with PSO, Quantum Particle Swarm Algorithm (QPSO) and Adaptive Particle Swarm Algorithm (APSO) using PUMA560 robotic arm and seven-degree-of-freedom robotic arm as the research object, and verifies the algorithm in terms of accuracy, running time, and convergence, and finally illustrates the characteristics of short time consumption and wide adaptability of this algorithm.