Time-Jerk Synthetic Optimal Trajectory Planning of Robot Using Strength Pareto Evolutionary Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 1141-1146
Author(s):  
Jie Yan ◽  
Dao Xiong Gong
Keyword(s):  
Evolutionary Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Speed ◽  
Mathematic Model ◽  
Stair Climbing ◽  
Planning Problem ◽  
Hexapod Robot ◽  
Strength Pareto Evolutionary Algorithm ◽  
Optimal Trajectory Planning

The Strength Pareto Evolutionary Algorithm (SPEA) is adopted to find time-jerk synthetic optimal trajectory of a hexapod robot in the joint space. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of via points to assure overall continuity of velocity and acceleration. Taken both the execution time and minimax approach of jerk as objectives, and expressed the kinematics constraints as upper bounds on the absolute values of velocity and acceleration, the mathematic model of time-jerk synthetic optimal trajectory planning is built. Finally, SPEA is adopted to optimize the stair-climbing trajectory of a hexapod robot, the simulation results show that this method can solve the trajectory planning problem effectively, and the stair-climbing trajectory can meet the contradictory objective functions of high speed and low robot vibration well.

scholarly journals A Method of Energy-Optimal Trajectory Planning for Palletizing Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yanjie Liu ◽  
Le Liang ◽  
Haijun Han ◽  
Shijie Zhang
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
High Speed ◽  
Optimization Problems ◽  
Load Capacity ◽  
Research Work ◽  
Extremum Problem ◽  
Multiple Shooting ◽  
High Load Capacity ◽  
Optimal Trajectory Planning

In this work, the energy-optimal trajectory planning and initial pick point searching problem for palletizing robot with high load capacity and high speed are studied, in which the pick point and place point of the robot are fixed to a desired location for each single task. These optimization problems have been transformed to ternary functional extremum problem and parameters optimal selection problem in which the performance index of the problems the rigid-flexible coupling dynamics model of the robot, and the constraint and boundary conditions of the robot are given. The fourth-order Runge-Kutta method, multiple shooting method, and traversing method are used to solve these specific mathematical problems. The effectiveness of the trajectory planning method is validated by the experimental and simulating results; thus the research work done here provides important support for subsequent palletizing robot research.

L2-Norm Optimal Trajectory Planning for Mobile Ground Vehicles in a Dynamically Changing Environment with both “Hard” and “Soft” Obstacles

2011 ◽  
Vol 130-134 ◽  
pp. 339-342
Author(s):  
Jian Yang ◽  
Mi Dong
Keyword(s):  
Boundary Conditions ◽  
Trajectory Planning ◽  
Design Parameter ◽  
Optimal Trajectory ◽  
Kinematic Model ◽  
Optimal Solution ◽  
Planning Problem ◽  
Ground Vehicles ◽  
Operational Environment ◽  
Optimal Trajectory Planning

In this paper, the real-time trajectory planning problem is considered for a differential vehicles in a dynamically changing operational environment. Some obstacles in the environment are not known apriori, they are either static or moving, and classified to two types: “hard” obstacles that must be avoided, and “soft” obstacles that can be run over/through. The proposed method presents trajectories, satisfying boundary conditions and vehicle’s kinematic model, in terms of polynomials with one design parameter. With a cost function ofL2norm, an optimal feasible trajectory is analytically solved for “hard” obstacles. By relaxing the optimal solution, “soft” obstacles are prioritized to be bypassed or overcome. The proposed method offers an automatic and systematic way of handling obstacles.The simulation is used to illustrate the proposed algorithm.

Quasi-optimal trajectory planning and control of a CRS A251 industrial robot

2002 ◽  
Vol 216 (4) ◽  
pp. 343-356
Author(s):  
J V Miro ◽  
A S White
Keyword(s):  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Industrial Robot ◽  
Minimum Principle ◽  
Terminal Phase ◽  
Planning Problem ◽  
Fundamental Parameter ◽  
Feedback Form ◽  
Open Chain ◽  
Optimal Trajectory Planning

A near-optimal solution to the path-unconstrained time-optimal trajectory planning problem is described in this paper. While traditional trajectory planning strategies are entirely based on kinematic considerations, manipulator dynamics are usually neglected altogether. The strategy presented in this work has two distinguishing features. Firstly, the trajectory planning problem is reformulated as an optimal control problem, which is in turn solved using Pontryagin's maximum/minimum principle. This approach merges the traditional division of trajectory planning followed by trajectory tracking into one process. Secondly, the feedback form compensates for the dynamic approximation errors derived from linearization and the fundamental parameter uncertainty of the dynamic equations of motion. This approach can cope with flexible robots as well as rigid links. The terminal phase of the motion is controlled by a feedforward controller to reduce chatter vibrations. Results from simulations and an on-line implementation to a general-purpose open-chain industrial manipulator, the CRS A251, confirm the validity of the approach and show that maximizing the capabilities of the device can lead to an overall improvement in the manipulator time response of up to 24 per cent, while retaining an acceptable overshoot and steady state error regime.

scholarly journals Solving the Time-Jerk Optimal Trajectory Planning Problem of a Robot Using Augmented Lagrange Constrained Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Shaotian Lu ◽  
Jingdong Zhao ◽  
Li Jiang ◽  
Hong Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Best Value ◽  
Augmented Lagrange Multiplier ◽  
Optimal Trajectory Planning

The problem of minimum time-jerk trajectory planning for a robot is discussed in this paper. The optimal objective function is composed of two segments along the trajectory, which are the proportional to the total execution time and the proportional to the integral of the squared jerk (which denotes the derivative of the acceleration). The augmented Lagrange constrained particle swarm optimization (ALCPSO) algorithm, which combines the constrained particle swarm optimization (CPSO) with the augmented Lagrange multiplier (ALM) method, is proposed to optimize the objective function. In this algorithm, falling into a local best value can be avoided because a new particle swarm is generated per initial procedure, and the best value gained from the former generation is saved and delivered to the next generation during the iterative search procedure to enable the best value to be found more easily and more quickly. Finally, the proposed algorithm is tested on a planar 3-degree-of-freedom (DOF) robot; the simulation results show that the algorithm is effective, offering a solution to the time-jerk optimal trajectory planning problem of a robot under nonlinear 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.

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