Non-uniform rational B-spline-based minimum cost trajectory planning for autonomous robots

This paper proposes a new novel trajectory planning method by using two evolutionary algorithms namely Elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for an autonomous robot manipulator (STANFORD robot) whose workspace includes several obstacles. The aim of the problem is to minimise a multicriterion cost function with actuator constraints, joint limits and obstacle avoidance constraints by considering dynamic equations of motion. Trajectories are defined by non uniform rational B-spline (NURBS) functions. This is a nonlinear constrained optimisation problem with 6 objective functions, 32 constraints and 288 variables. The multicriterion cost function is a weighted balance of transfer time, mechanical energy of the actuators, singularity avoidance, penalty function to guarantee the collision free motion, joint jerks and joint accelerations. All types of obstacles (fixed, moving and oscillating obstacles) are present in the workspace of the robot. The results obtained from NSGA-II and DE are compared and analysed.