Abstract
In this paper, a new method of online planning high smooth and time-optimal trajectory for robotic manipulators that applies an adaptive elite genetic algorithm with singularity avoidance (AEGA-SA) is presented. The strategy is designed as a combination of the time-optimal trajectory planning with quintic polynomial in Cartesian space. For improving optimization performance, elitist group and adaptive adjustment mechanisms are used based on genetic algorithm (GA) framework. In the meantime, GA is combined with singularity avoidance mechanism to avoid the singularities appearing in the trajectory, improves the recognition capability of optimum solution. Experimental results show that, the proposed approach is more effective and better performance than the original GA and its variants, with ensuring a both smooth and efficiency performance for the robotic manipulators.
Highlights
Trajectory planning is a very popular and valuable benchmark problem in robotic manipulators [1]–[3]
This paper proposes an adaptive elite genetic algorithm with singularity avoidance (AEGA-SA) algorithm for online timeoptimal trajectory planning of robotic manipulators
Singularity avoidance is combined to avoid the singularities appearing in the online Cartesian space trajectory planning, improves the recognition capability of the best runtime
Summary
Trajectory planning is a very popular and valuable benchmark problem in robotic manipulators [1]–[3]. Trajectory can be planned either in Cartesian space or in joint space to make the motion of the robot smooth and continuous. Trajectory planning in Cartesian space can be planned online without interpolation for joints which is more intuitive and accurate than planning in joint space [4]–[6]. Large amount of kinematic calculation, singularities of the joints are following. With the development of computer technology, the realtime calculation of kinematics is improved [7]. Considering the requirement to increase productivity in automatic production line, this paper aims at online time-optimal trajectory planning in Cartesian space
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