Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

2016 ◽  
Vol 89 (1-4) ◽  
pp. 389-406 ◽  
Author(s):  
Fares J. Abu-Dakka ◽  
Iyad F. Assad ◽  
Rasha M. Alkhdour ◽  
Mohamed Abderahim
Keyword(s):  
Evolutionary Algorithm ◽  
Trajectory Planning ◽  
Statistical Evaluation ◽  
Industrial Robots ◽  
Minimum Time ◽  
Planning Problem

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

Robotica ◽  
2014 ◽  
Vol 33 (3) ◽  
pp. 669-683 ◽  
Author(s):  
Fares J. Abu-Dakka ◽  
Francisco J. Valero ◽  
Jose Luis Suñer ◽  
Vicente Mata
Keyword(s):  
Trajectory Planning ◽  
Direct Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Computational Time ◽  
Planning Problem ◽  
Complex Environments ◽  
Operational Parameters ◽  
Algorithm Efficiency ◽  
Planning Problems

SUMMARYThis paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

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.

Smooth point-to-point trajectory planning for robot manipulators by using radial basis functions

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 539-559 ◽  
Author(s):  
Taha Chettibi
Keyword(s):  
Radial Basis Functions ◽  
Trajectory Planning ◽  
Robot Manipulators ◽  
Smooth Point ◽  
Basis Functions ◽  
Minimum Time ◽  
Planning Problem ◽  
Planning Techniques ◽  
Radial Basis ◽  
Point To Point

SUMMARYThe paper introduces the use of radial basis functions (RBFs) to generate smooth point-to-point joint trajectories for robot manipulators. First, Gaussian RBF interpolation is introduced taking into account boundary conditions. Then, the proposed approach is compared with classical planning techniques based on polynomial and trigonometric models. Also, the trajectory planning problem involving via-points is solved using the proposed RBF interpolation technique. The obtained trajectories are then compared with those synthesized using algebraic and trigonometric splines. Finally, the proposed method is tested for the six-joint PUMA 560 robot in two cases (minimum time and minimum time-jerk) and results are compared with those of other planning techniques. Numerical results demonstrate the advantage of the proposed technique, offering an effective solution to generate trajectories with short execution time and smooth profile.

Evolutionary Collision Free Optimal Trajectory Planning for Mobile Robots

1999 ◽  
Vol 11 (2) ◽  
pp. 153-164 ◽  
Author(s):  
M.M.A. Hashem ◽  
◽  
Keigo Watanabe ◽  
Kiyotaka Izumi ◽  
◽  
...  
Keyword(s):  
Mobile Robots ◽  
Evolutionary Algorithm ◽  
Trajectory Planning ◽  
Fitness Function ◽  
Coordinate Frame ◽  
Planning Problem ◽  
Evolutionary Trajectory ◽  
Optimum Trajectory ◽  
Insertion And Deletion ◽  
Sensor Modeling

We present an evolutionary trajectory planning method for mobile robots following a novel evolution strategy (NES) algorithm. The 2-D trajectory planning problem of a mobile robot among polygonal obstacles is formulated as a constrained time-optimum control problem considering motion. Unlike traditional evolutionary representation, special representation of individuals and crossover are used for the evolutionary search. Swapping crossover, insertion, and deletion mutations are used as background operators for maximum evolutionary algorithm flexibility. Polygonal obstacles in the world coordinate frame are modeled as circles from visibility and sensor modeling concepts. An appropriate cost (fitness) function is constructed as the only link between the evolutionary algorithm and environment. Our proposed evolution is effective for collision-free optimum trajectory planning in robot simulation within a heavily constrained (obstacle) environment.

Validation of Minimum Time-Jerk Algorithms for Trajectory Planning of Industrial Robots

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
A. Gasparetto ◽  
A. Lanzutti ◽  
R. Vidoni ◽  
V. Zanotto
Keyword(s):  
Dynamic Model ◽  
Trajectory Planning ◽  
Experimental Analysis ◽  
Execution Time ◽  
Industrial Robot ◽  
Experimental Tests ◽  
Industrial Robots ◽  
Minimum Time ◽  
Mechanical Constraints ◽  
Planning Algorithm

In this paper, an experimental analysis and validation of a minimum time-jerk trajectory planning algorithm is presented. The technique considers both the execution time and the integral of the squared jerk along the whole trajectory, so as to take into account the need for fast execution and the need for a smooth trajectory, by adjusting the values of two weights. The experimental tests have been carried out by using an accelerometer mounted on a Cartesian robot. The algorithm does not require a dynamic model of the robot, but just its mechanical constraints, and can be implemented in any industrial robot. The outcomes of the tests have been compared with both simulation and experimental results yielded by two trajectory planning algorithms taken from the literature.

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