This paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

The paper presents a geometric method for collision-free manipulator path planning in 3D Euclidean space with polyhedral obstacles. It ensures that none of the links nor the manipulator tip collide with the objects. The method is computationally very cheap and it does not require intensive off-line preprocessing. Hence, it is real-time applicable if the information about obstacles positions and shapes is obtained from a higher control level. The trajectories generated lie within the reachable workspace. The method is implemented on a VAX 11/750 computer and the simulation results are included.