Motion Planning of a Robotic Arm using an Adaptive Linear Interpolation Crossover and Variable-Length Move Sequence Genome

The ability to perform robotic arm motion planning is a necessity in the design of autonomous and intelligent robotic systems. Motion planning allows the autonomous robotic arm to maneuver its end-effector in an unstructured environment whilst avoiding obstacles on the workspace. This ability is particularly important in processes with pick-and-place operations and varying object positions. In this study, a genetic algorithm-based motion planning for a 4-DOF robotic arm was developed. The developed genetic algorithm operates on a variable-length genome that consists of changes in joint angles. These changes in joint angles represent the end-effector's move sequence. The results show that adaptive linear interpolation crossover (ALIX) improves the convergence of the motion path towards minimization of end-effector error and path length. On average, the end-effector error is 1.4 mm with a maximum path length deviation from a straight line of about 50.4 mm tested on extreme target points. Testing with obstacles present in the workspace shows the ability of the algorithm to generate solution paths to avoid them as well.