Optimal trajectory planning for robotic manipulators using improved teaching-learning-based optimization algorithm

The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm.

The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory.

Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method.

The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.