A New Heuristic Approach for Inverse Kinematics of Robot Arms

Inverse kinematics of a robot arm has become very important research area in recent decades. Also, the use of bio-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Harmony Search (HS) has been expeditiously increasing to handle many optimization problems. In this paper, an new approach based on the modified Artificial Bee Colony (ABC) has been proposed to solve inverse kinematics problem of robot arms. For this purpose, we defined a cost function based on the Euclidian distance from a position to the target in Cartesian space. We obtained a fitness function from the cost function, and used it in the modified ABC to minimize the fitness value. Also we used some statistical analysis methods to determine the trade-off parameters of the modified ABC. The simulation results show that proposed approach has better performance in terms of both position accuracy and the solution time than the previous studies employing the other heuristic methods like PSO and HS.