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Comparison of Three Meta-heuristic Algorithms for Solving Inverse Kinematics Problems of Variable Curvature Continuum Robots

Since the analytical solutions of kinematics problems of continuum robots, especially those having a complex form, are not yet available, an alternative method is to obtain fast and accurate solutions using meta-heuristic algorithms. In this paper, we present a comparison between the use of three meta-heuristic algorithms namely: Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving the inverse kinematic problems of continuum robots so-called variable curvature continuum robots. Simulation analysis is performed through Matlab Software which shows the performance of the studied algorithms in terms of the computation time and accuracy during path tracking. It is found that the developed code on Matlab Software for the three meta-heuristic algorithms can perfectly imitate the behavior of continuum robots which makes it a realistic-like environment for the simulation analysis. Concerning the efficiency of the developed meta-heuristic algorithms, ABC algorithm provides a remarkable accuracy for the tracking of the prescribed trajectories yet it takes time for the accomplishment of the prescribed task. For GA and PSO, they are suitable when it comes to real time application compared to ABC.