Evolutionary Path Planning Algorithm for Industrial Robots

Abstract

This paper proposed a new methodology to solve collision free path planning problem for industrial robot using genetic algorithms. The method poses an optimization problem that aims to minimize the significant points traveling distance of the robot. The behavior of more two operational parameters -the end effector traveling distance and computational time -are analyzed. This algorithm is able to obtain the solution for any industrial robot working in the complex environments, just it needs to choose a suitable significant points for that robot. An application example has been illustrated using robot Puma 560.

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

“…The mutation will not affect the coordinates that has not changed at all, for example the x-coordinate in (0,0,0),(0,0,1),(0,1,1) since any changes will result in the trajectory being invalid. For this new position, the ACs algorithm will take places to move the robot from the position (0, 0, 0) to (0, 0, 1) and then to (1,1,2).This process is illustrated in Figure 5. These values were used for the algorithm testing.…”

“…A clear difference exists between path and trajectory planning, as well as the algorithms used to solve these problems. In the case of path planning, the algorithms try to obtain a sequence of configurations between the initial and final ones, fulfilling some kinematic and geometrical constraints [1]. On the other hand, the algorithms for trajectory planning try to obtain a temporal history of the evolution of robot joint coordinates, by minimizing aspects such as time, power, and/or energy consumption.…”