Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles

Abu‐Dakka, Fares J.; Rubio, Francisco; Mata, Vicente

doi:10.1016/j.euromechsol.2013.05.007

Cited by 30 publications

(22 citation statements)

References 21 publications

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“…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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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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“…In order to avoid interference, the AB, CE, and GE segments are set as linear trajectories. In some former researches [1][2][3][4][5][6][7], the trajectory is usually optimized according to a number of fixed teaching points and the way of inserting fixed via points. The optimization method overreliance on the selection of the initial trajectory is mainly to optimize the segmentation trajectory between the fixed via points.…”

Section: Optimization Modelmentioning

confidence: 99%

“…Korayem et al converted dynamic load-carrying capacity problem into a trajectory optimization problem of cable-suspended parallel robots which is fundamentally a constrained nonlinear optimization 2 Mathematical Problems in Engineering problem [6]. Abu-Dakka et al addressed an indirect method for trajectory planning for industrial robots operating in workspaces with obstacles using an evolutionary algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

Liang

Liu

2018

Mathematical Problems in Engineering

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Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment.

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“…They aimed in controlling what they call the "explosion" of the swarm that leads to the divergence of the PSO. Clerc and Kennedy's concept led to update the equation of velocity in (8) to become as follows:…”

Section: Pso With Constriction Factor (Pso-c)mentioning

confidence: 99%

“…It depends on complex mathematical model, and suffers from common drawbacks such as the limitation to simple two-dimension space, local minima, incompleteness and high computational time, produces long and rough paths resulting from a compilation of straight line which cannot be executed by the robot. On the other hand, the meta-heuristic class that group neural network [5,6], fuzzy logic [7], evolutionary algorithms [8] (i.e., genetic algorithm, genetic programming, evolutionary programming and evolution strategy), ant colony [9] and particle swarm [10][11][12][13][14] emerge to overcome the shortcomings of the conventional class. These algorithms are generally population based that make a multi exploring search; deals with the problem of local minima and the high class of configuration space; do not call for gradient, high order derivatives or initial estimation of solution.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning

Zerrouki

Goléa

Benoudjit

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Keywords robot manipulators, path planning, B-splines, particle swarm optimization IntroductionIn recent years as the technology advanced the use of robots become common and human life become easier. Months or even years needed to cover long distances on feet or using animals have been reduced to hours using planes. Planets which appear in nights, and seen only by eyes has been explored by human using planetary robots. Under sea that represent puzzles for human-being have been excavated using under-water robots. Complex hard works that need many workers and long period to be accomplished have become short time tasks through exploiting industrial robots. Chirurgical operations that need the presence of doctors and nurses are simplified by taking advantage of service robots.All kinds of robots whether they are mobile or manipulators operate in unknown environments or in environments that changed continuously. Therefore, they require an important step to accomplish their tasks even though simple or complex. This step consists of generating a path that allows the robots to navigate through, without colliding with any of the surrounding obstacles. Besides, the path should start form an initial position and reach the final one. The main aim behind path planning for mobile robots is to make them capable of reacting to any new situation they face, thus increase their autonomies, whereas for manipulators path planning is necessary for high speed, high precision and consequently for increasing productivity and safeness.Generally, existing approaches for solving this problem of path planning can be classified into two classes: conventional and meta-heuristic. On the one hand, the conventional class includes bug algorithms, potential field [1], road map [2,3], cell decomposition and sampling based algorithms [4]. It depends on complex mathematical model, and suffers from common drawbacks such as the limitation to simple two-dimension space, local minima, incompleteness and high computational time, produces long and rough paths resulting from a compilation of straight line which cannot be executed by the robot. On the other hand, the meta-heuristic class that group neural network [5,6], fuzzy logic [7], evolutionary algorithms [8] (i.e., genetic algorithm, genetic programming, evolutionary programming and evolution strategy), ant colony [9] and particle

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Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles

Cited by 30 publications

References 21 publications

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

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

Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning

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