Kinematics and trajectory planning analysis based on hybrid optimization algorithms for an industrial robotic manipulators

Abstract

In industrial applications and automation, the robotic manipulators exhibit a significant role. Several complex robotic systems performed a number of industrial works named spray painting, welding, assembly, pick and place action etc. The end-effector's position and the joint angles are plays a vital role since any task is activated inside the pre-defined robotic manipulator's work space. Also, the problem of trajectory planning is a very challenging task in the robotic fields.To solve these problems, this paper proposes a kinematics analysis and trajectory planning of an Industrial robotic manipulators (IRMs) based on the hybrid optimization algorithms. Here, three IRMs such as PUMA 560 (6 DOF), KUKA LBR iiwa 14 R820 (7DOF) and ABB IRB 140 (6DOF) are considered. For each robot, the forward and the inverse kinematics (IK) are analyzed and also the trajectory planning of each robot is discussed using the hybrid optimization algorithms. In this work, 18 optimization algorithms such as PSO (particle swarm optimization), SSO (social spider optimization), DFO (dragonfly optimization), BOA (butterfly optimization), CSA (crow search algorithm), BSA (bird swarm algorithm), SHO (selfish herd optimization), KHO (krill herd optimization), ALO (antlion optimization), ACO (ant colony optimization), GWO (Grey wolf optimization), GOA (grasshopper optimization), SBO (satin bowerbird optimizer), WCO (world cup optimization), COA (cuckoo optimization algorithm), CFA (cuttlefish algorithm), SOA (seagull optimization algorithm), and TSA (tunicate swarm algorithm) are utilized for both forward and inverse kinematic analysis and the trajectory planning problem. For the PUMA 560, KUKA LBR iiwa 14 R820, and ABB IRB 140 IRMs, forward kinematics (FK) are solved by the hybrid combination of PSO-SSO, SHO-KHO, and SBO-WCO individually. Also the IK are solved by the DFO-BOA, ALO-ACO and COA-CFA for each IRMs. The trajectory planning problem is solved by the CSA-BSA, GWO-GOA and SOA-TSA for each robot individually. These optimization techniques gives number of solution for kinematics and trajectory problem but it converses the best solution for the minimum multi-objective function value. The advantages of these hybrid algorithm are shorter computation time, and fewer iterations. The kinematics and trajectory analysis of each IRM is simulated using robotic tool box in MATLAB with GUI interface. For each IRM, optimized position values of the end effector, joint angles and the best optimal path are computed with minimum objective function. Finally, the performance of each robot is compared to the existing robotic works.

Design and development of a cost efficiency robot arm with a PLC-based robot controller

“…Designing and developing a robot arm is a challenging task. It requires a wide range of knowledge about mechanical design, structural analysis [4][5][6][7][8], electronics and control [9][10], kinematic modeling and analysis, path planning, and trajectory tracking [11][12][13].…”

A Multi-Objective Trajectory Planning Method of the Dual-Arm Robot for Cabin Docking Based on the Modified Cuckoo Search Algorithm

“…Meanwhile, two search strategies were also given, and the local and global search capabilities were enhanced. Later, the trajectory planning problems were solved by the Cuckoo Optimization Algorithm (COA), Cuttlefish Algorithm (CFA), Seagull Optimization Algorithm (SOA) and Tunicate Swarm Algorithm (TSA) [20].…”

Research Progress on Trajectory Planning of Industrial Robots

“…Different from the intuitiveness of Cartesian space trajectory planning, the results of joint space trajectory planning are detailed variable data. When entering the robot controller, the complex inverse solution process can be avoided [5][6][7][8], and the results of joint space trajectory planning can be directly input, and some robots can be avoided. Singular position during trajectory operation.…”