Swarm intelligence application in solving robot inverse kinematic problems

This dissertation aims to find the inverse kinematic solution for redundant serial manipulators using the meta-heuristic method, Particle Swarm Optimization algorithm (PSO). Primarily this paper focuses on moving the end-effector to any desired pose in cartesian space accurately by converging position and orientation with the PSO algorithm. In order to prove the exactness of the study, the result has been compared with some of other PSO research that only examines converging the position. All demonstrations were performed by using humanoid human-sized with 7 degrees of freedom robot (DOF), Baxter. First, the Denavit-Hartenberg(DH) table of Baxter's left arm is created, and transformation matrices are calculated according to two different setups joint angles to calculate target position and orientation values. Furthermore, joint angles are picked randomly for each particle, and the particles' pose is calculated by applying forward kinematics. In order to obtain subsequent angle values, the PSO algorithm, conversion of quaternion to a rotation matrix, and Jacobian matrices are utilized. This research gives another perspective to solving inverse kinematic by using quaternions instead of Euler angles. The Euclidian function is used to compute the cost function, which estimates the distance between the target pose and particle's pose. In this study, the algorithm is tested with several different concepts. Conclusively, the validity of the algorithm is verified via Gazebo simulation. The result confirms that the algorithm functions well in accuracy and merit of the swarm intelligence in solving the inverse kinematics problem for any serial robotic manipulators.