An algorithm for smooth trajectory planning optimization of isotropic translational parallel manipulators

This paper presents a new algorithm for smooth trajectory planning optimization of isotropic translational parallel manipulators (ITPM) that their Jacobian matrices are constant and diagonal over the whole robot workspace. The basic motivation of this work is to formulate the robot kinematic and geometric constraints in terms of optimization variables to reduce the mathematical complexity and running time of the resulting algorithm which are important issues in trajectory planning optimization. To achieve this aim, the end-effector trajectory of ITPMs in Cartesian space is defined using fifth-order B-Splines, and as a main contribution, all of the actuators limitations and robot constraints are formulated in terms of B-Spline parameters with no need of any information about the workspace geometry. Then the total required energy, total time of motion, and maximum absolute value of actuators’ jerk are defined as objective functions and non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve the resulting nonlinear constrained multi-objective optimization problem. Finally, the proposed algorithm is implemented in MATLAB software for Cartesian parallel manipulator (CPM) as a case study, and the results are demonstrated and discussed. The obtained results show the significant performance of the proposed algorithm with no need to evaluate the robot’s constraints and boundaries of its workspace in each point of the end-effector trajectory.