Abstract
By using quintic polynomial function to interpolate several given points of each joint of the robot, the mathematical expressions of each joint variable of the robot with time are established. In addition, to improve the search algorithm performance crossover operator and mutation operator of the genetic algorithm are improved in cosine form. Furthermore, the improved adaptive genetic algorithm is applied to optimize the time interval of interpolation points of each joint, so as to realize time optimal trajectory planning. Moreover, MATLAB simulation is carried out, and the results show that the method proposed in this paper reduces the running time of the robot tasks. Meanwhile, the curves of angle position, velocity and acceleration of each joint are smooth enough, which ensure accomplish its tasks in a stable and efficient way.
Highlights
Robot trajectory planning usually refers to track points given several expectations and target pose, and timely adjust the rotation angle of each joint of the robot to the end effector at a prescribed trajectory followed by each point to eventually reach the target point
The track points are used to carry out interpolation operation using various spline functions, polynomial functions or other forms of curves, and the expressions about the time of each joint variable for the robot are obtained
In reference literature [18], a cubic B-spline curve is used to interpolate the robot motion trajectory, and an improved genetic algorithm based on the crossover operator and mutation operator adjusted with evolutionary algebraic average fitness is used to perform the time optimal trajectory planning for the robot motion trajectory
Summary
Robot trajectory planning usually refers to track points given several expectations and target pose, and timely adjust the rotation angle of each joint of the robot to the end effector at a prescribed trajectory followed by each point to eventually reach the target point. In reference literature [18], a cubic B-spline curve is used to interpolate the robot motion trajectory, and an improved genetic algorithm based on the crossover operator and mutation operator adjusted with evolutionary algebraic average fitness is used to perform the time optimal trajectory planning for the robot motion trajectory. It can be seen that the improved genetic algorithm, which the crossover operator and the mutation operator in the general adaptive genetic algorithm are adjusted cosine, based on quintic polynomial interpolation described in this paper satisfies the goal of the shortest time trajectory planning.
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