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

Abu‐Dakka, Fares J.; Valero, Francisco; Suñer, Jose Luis; Mata, Vicente

doi:10.1017/s0263574714000393

Cited by 28 publications

(17 citation statements)

References 29 publications

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“…In addition, some other scholars have also adopted genetic algorithm to optimize the manipulator trajectory planning. 31,32 It is worth noting that the intermediate point solved by genetic algorithm is not necessarily the optimal solution. But it is one of many solutions to the domain near the optimal solution under the condition of constraints.…”

Section: Gene Optimization Schemementioning

confidence: 99%

An intermediate point obstacle avoidance algorithm for serial robot

Chen

et al. 2018

Advances in Mechanical Engineering

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The trajectory planning and obstacle avoidance of serial robot is an important area of research and efficient optimization algorithms are needed. This article proposes an intermediate point obstacle avoidance algorithm to ensure smooth trajectory and efficient movement during the obstacle avoidance process. Using the intermediate point obstacle avoidance algorithm, the robotic arm can steer obstacle around with the minimum joint space variations and the shortest end motion trajectory. We also design a novel collision detection strategy and fitness evaluation function based on genetic algorithm to optimize the intermediate point parameters. In order to evaluate the performance of the intermediate point obstacle avoidance algorithm, we have conducted three typical experiments: single obstacle, multiple obstacles, and no obstacle. The experimental results demonstrate that the proposed method can not only effectively realize the obstacle avoidance, but also efficiently improve the movement performance of the robotic arm.

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Section: Gene Optimization Schemementioning

confidence: 99%

An intermediate point obstacle avoidance algorithm for serial robot

Chen

et al. 2018

Advances in Mechanical Engineering

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“…The vector r (shown in Figure 2(b)) is the location of the center of mass of a link relative to the frame representing the link. The rotation angle of 1 , 2 , 3 , 4 , and 5 is represented by 1 Figure 2: The assigned reference frame: (a) D-H reference frame and (b) the location of the center of mass.…”

Section: Forward Kinematicsmentioning

confidence: 99%

“…There are a number of popular methods for transcribing the trajectory planning problem into an optimization problem. Essentially, these transcriptions fall into two categories: direct (global) methods (such as [1][2][3][4]) and indirect (decoupled) methods (e.g., [5][6][7][8]).…”

Section: Introductionmentioning

confidence: 99%

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Guo¹,

Li²,

Cao³

et al. 2016

Mathematical Problems in Engineering

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A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC) is presented for a 5-degree of freedom (DOF) hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions) subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II). A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

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“…Therefore, the trajectory optimization of the plasma robot is mainly the trajectory control of the joint space of the redundant robot, so that the trajectory of the end of the lance and the desired trajectory of the working base are Match. Due to the redundant nature of the plasma robot, there are numerous optimization inverse solutions in the joint space, which makes the movement characteristics of the plasma robot in the joint space very complicated, which makes the end of the gun movement path difficult to describe [13][14][15][16]. In this paper, some reference points are selected based on the desired trajectory, and the inverse kinematics is solved to obtain the inverse solution of joint space optimization.…”

Section: Introductionmentioning

confidence: 99%

Trajectory control and parameter optimization of plasma robot gun

2020

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A plasma robot model with redundant freedom is proposed for the shortage of flexibility in the complex working environment of plasma robot. The existence of redundant degrees of freedom leads to the complex motion characteristics of the plasma robot joints. The trajectory of the end of the spray gun is difficult to describe. Therefore, a trajectory optimization algorithm based on fitness function is proposed. In the plasma robot joint space, a parabolic linear fitting is adopted. The error between the actual trajectory and the desired trajectory is taken as fitness function. Genetic algorithm is applied to find the optimal solution for the parameters in trajectory planning. The orthogonal experimental model of the parameters of the plasma spray gun is set up. The optimum working parameters of the spray gun are obtained through the analysis and study of the power, atmospheric pressure and the distance of the gun. Finally, the rationality of the proposed method is proved by simulation and test.

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

Cited by 28 publications

References 29 publications

An intermediate point obstacle avoidance algorithm for serial robot

An intermediate point obstacle avoidance algorithm for serial robot

A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

Trajectory control and parameter optimization of plasma robot gun

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