The Research of Timing-Optimal Trajectory Planning Based on Improved Genetic Algorithms

2013 ◽  
Vol 433-435 ◽  
pp. 562-565 ◽  
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Operation Process ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. Program the operation process in the MATLAB software. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained by the improved genetic algorithms under the kinematical constraints.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

scholarly journals Time-jerk optimal trajectory planning of hydraulic robotic excavator

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110346
Author(s):  
Yunyue Zhang ◽  
Zhiyi Sun ◽  
Qianlai Sun ◽  
Yin Wang ◽  
Xiaosong Li ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Efficiency ◽  
Optimal Solution ◽  
Optimal Time ◽  
Target Point ◽  
Robotic Excavator ◽  
The Impact ◽  
Optimal Trajectory Planning

Due to the fact that intelligent algorithms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are susceptible to local optima and the efficiency of solving an optimal solution is low when solving the optimal trajectory, this paper uses the Sequential Quadratic Programming (SQP) algorithm for the optimal trajectory planning of a hydraulic robotic excavator. To achieve high efficiency and stationarity during the operation of the hydraulic robotic excavator, the trade-off between the time and jerk is considered. Cubic splines were used to interpolate in joint space, and the optimal time-jerk trajectory was obtained using the SQP with joint angular velocity, angular acceleration, and jerk as constraints. The optimal angle curves of each joint were obtained, and the optimal time-jerk trajectory planning of the excavator was realized. Experimental results show that the SQP method under the same weight is more efficient in solving the optimal solution and the optimal excavating trajectory is smoother, and each joint can reach the target point with smaller angular velocity, and acceleration change, which avoids the impact of each joint during operation and conserves working time. Finally, the excavator autonomous operation becomes more stable and efficient.

A Novel Algorithm for Time Optimal Trajectory Planning

2014 ◽  
Author(s):  
Mingxing Yuan ◽  
Bin Yao ◽  
Dedong Gao ◽  
Xiaocong Zhu ◽  
Qingfeng Wang
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Problem Of Time ◽  
High Productivity ◽  
Hard Constraints ◽  
Time Optimal ◽  
Rate Optimization ◽  
Feed Rate Optimization ◽  
Optimal Trajectory Planning

Time optimal trajectory planning under various hard constraints plays a significant role in simultaneously meeting the requirements on high productivity and high accuracy in the fields of both machining tools and robotics. In this paper, the problem of time optimal trajectory planning is first formulated. A novel back and forward check algorithm is subsequently proposed to solve the minimum time feed-rate optimization problem. The basic idea of the algorithm is to search the feasible solution in the specified interval using the back or forward operations. Four lemmas are presented to illustrate the calculating procedure of optimal solution and the feasibility of the proposed algorithm. Both the elliptic curve and eight profile are used as case studies to verify the effectiveness of the proposed algorithm.

The Applications of Improved Genetic Algorithm on Hypersonic Vehicle Trajectory Optimization

2012 ◽  
Vol 466-467 ◽  
pp. 1095-1099
Author(s):  
Liu Xu ◽  
Wei Min Li ◽  
Lin Zhang ◽  
An Tang Zhang
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
High Sensitivity ◽  
Initial Guess ◽  
Cruise Missile ◽  
Trajectory Design ◽  
Improved Genetic Algorithm ◽  
Vehicle Trajectory

The Optimal trajectory design for hypersonic cruise missile is an optimal control problem with strict terminal constraints and variable final time. The classical algorithms always encounter the problems of high sensitivity to initial guess and local convergence in solving this problem. Aiming at these problems, genetic algorithm (GA) which is of good global convergence is applied to designing the optimal trajectory for hypersonic cruise missile. In order to improve the convergence rate of GA and overcome its premature problems, this text introduces a predatory search (PS) strategy to speed the convergence of genetic algorithms, robust and closer to the optimal solution. This text compares the original genetic algorithm (GA) and improved genetic algorithm by the emulate experiments, and the results show that the PSGA is a more effective method to design the Optimal trajectory for hypersonic cruise missile than the original genetic algorithm.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

Energy Optimal Trajectory Planning of a Robotic Manipulator Using Genetic Algorithm

2010 ◽  
Author(s):  
Aritra Biswas ◽  
B. L. Deekshatulu ◽  
Shibendu Shekhar Roy ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Robotic Manipulator ◽  
Optimal Trajectory Planning
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