Genetic Algorithms based method for time optimization in robotized site

Baizid, Khelifa; Chellali, Ryad; Yousnadj, Ali; Meddahi, Amal; Bentaleb, Toufik

doi:10.1109/iros.2010.5651948

Cited by 18 publications

(19 citation statements)

References 12 publications

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“…Zacharia et al [4] involved the robot kinematics and applied a Genetic Algorithm to optimize the task sequence. Baizid et al [5] extended their approach by optimizing location of the robot's base. Saha et al [10] proposed the approach that involves collision-free path planning into the problem of task sequence calculation.…”

Section: A Robotic Backgroundmentioning

confidence: 99%

“…One way to minimize the cost is to optimize the task sequence. There are many approaches that aim at minimizing the sequence of strictly defined tasks and also considering robot kinematics, e.g., [4] [5]. Some approaches exploit the freedom of effective tasks execution for optimization, however, they ignore the robot kinematics and use Euclidean metric instead, e.g., [6] [7].…”

Section: Introductionmentioning

confidence: 99%

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Improving the sequence of robotic tasks with freedom of execution

Alatartsev

Ortmeier

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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An industrial robot's workflow typically consists of a set of tasks that have to be repeated multiple times. A task could be, for example, welding a seam or cutting a hole. The efficiency with which the robot performs the sequence of tasks is an important factor in most production domains. In most practical scenarios, the majority of tasks have a certain freedom of execution. For example, closed-contour welding task can often be started and finished at any point of the curve. In this paper we propose a method that is able to automatically improve the given sequence of robotic tasks that allow for a certain freedom in (i) the position of the starting point along the curve, (ii) the orientation of the end-effector and (iii) the robot configuration. The proposed approach does not depend on the production domain and could be combined with any algorithm for constructing the initial task sequence. We evaluate the algorithm on a realistic case study and show that it could significantly improve the production time on the test instances from the cutting-deburring domain.978-1-4799-6934-0/14/$31.00 ©2014 IEEE

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Section: A Robotic Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the sequence of robotic tasks with freedom of execution

Alatartsev

Ortmeier

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…The first part represents the waypoint sequence, while the second part represents the sequence of the joint configurations at the waypoints, which corresponds to the first part. In [23], each chromosome consists of three parts, the waypoint sequence, the sequence of joint configurations, and the robot placement. The technique of dividing each chromosome into several parts was also employed in [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…The technique of dividing each chromosome into several parts was also employed in [24][25][26]. A common point of IGAs in [22][23][24][25][26] is that the parameter denoting the joint angular velocity is predefined as a constant. The other category of approaches aims at dividing MTTP into several subproblems which are then solved successively.…”

Section: Introductionmentioning

confidence: 99%

Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm

2019

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This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict the joint trajectories. An improved genetic algorithm (IGA) is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts, namely the waypoint sequence, the sequence of the joint configurations, and a special value for the depiction of the joint trajectories. Numerical simulations, including comparisons with two other approaches, are developed to test IGA validity.

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“…Recent papers like [14]- [16] also present GAs for the optimization of tasks and configurations of modular homogeneous reconfigurable robots and manipulators.…”

Section: Introductionmentioning

confidence: 99%

Offline GA-Based Optimization for Heterogeneous Modular Multiconfigurable Chained Microrobots

Brunete

Hernando

Gambao

2013

IEEE/ASME Trans. Mechatron.

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Abstract-This paper presents a GA-based optimization procedure for bioinspired heterogeneous modular multiconfigurable chained microrobots. When constructing heterogeneous chained modular robots that are composed of several different drive modules, one must select the type and position of the modules that form the chain. One must also develop new locomotion gaits that combine the different drive modules. These are two new features of heterogeneous modular robots that they do not share with homogeneous modular robots. This paper presents an offline control system that allows the development of new configuration schemes and locomotion gaits for these heterogeneous modular multiconfigurable chained microrobots. The offline control system is based on a simulator that is specifically designed for chained modular robots and allows them to develop and learn new locomotion patterns.

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Genetic Algorithms based method for time optimization in robotized site

Cited by 18 publications

References 12 publications

Improving the sequence of robotic tasks with freedom of execution

Improving the sequence of robotic tasks with freedom of execution

Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm

Offline GA-Based Optimization for Heterogeneous Modular Multiconfigurable Chained Microrobots

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