Integrated Optimization of Trajectory Planning for Robot Manipulators Based on Intensified Evolutionary Programming

Optimal trajectory planning for robot manipulators is always the hot spot in the research field of robotics. The performance indexes used in optimal trajectory planning are classified into two main categories. One is called optimum traveling time. The other is called optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. Unfortunately, there are few planning algorithms designed to satisfy two performance indexes simultaneously. There are some deficiencies appeared in the existing integrated optimization algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators.

I. Introduction

Optimal control for robot manipulators plays a very important role in the research of robotics. It can be divided into two subproblems: the optimal trajectory planning (OTP); and the trajectory tracking and control (TTC). The latter leads robots to follow the control commands that are deduced by the former as closely as possible [1]. The trajectory tracking and control is relatively simple and can be achieved by adopting some existing approaches, such as sliding mode controller (SMC) [2], etc. However, since there are many complex constraint conditions concerning robot kinematics and dynamics, the corresponding algorithms for solving OTP problem have been improved in recent years. Therefore, we will focus our discussion on how to solve the OTP problem effectively in this paper.