Grid modeling of robot cells: A memory-efficient approach

Boschian, Valentina; Pruski, Alain

doi:10.1007/bf01257995

Cited by 25 publications

(12 citation statements)

References 12 publications

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“…There are many classic approaches for the path planning problem, e.g., the potential field methods [22], visibility graph methods [42], and grid methods [8]. These classic approaches have a big disadvantage of being trapped in local minima, which makes them inefficient in practice [48].…”

Section: B Path Planning In Dynamic Environmentsmentioning

confidence: 99%

A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

Yang

2012

IEEE Trans. Evol. Computat.

160

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To solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark.

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Section: B Path Planning In Dynamic Environmentsmentioning

confidence: 99%

A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

Yang

2012

IEEE Trans. Evol. Computat.

160

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“…8,9,10,11,12 Another kind of method usually used is a graphical method, for example, viewable method, raster method, and the Voronoi diagram method. 13,14,15 The mentioned traditional methods and graphical methods have some shortcomings, for example, the simulated annealing algorithm converges very slowly, the APF method finds it easy to fall into local optimality and the end point could be unreachable, for the A* algorithm and the Voronoi diagram method, the computing costs are very high. With the increase of the complexity of the environments and the difficulty of the task, the traditional methods usually find it difficult to achieve ideal performance.…”

Section: Introductionmentioning

confidence: 99%

A cubic spline method combing improved particle swarm optimization for robot path planning in dynamic uncertain environment

Wen

Tan

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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This article considers a robot path planning problem originated from a robot factory inspection scenario. In the problem, the robot is in a dynamic uncertain environment, that is, a moving target object and several static and dynamic obstacles. An inertial positioning strategy is proposed to enable the robot to predict the position of the target in advance. From this predicted position, the robot path is generated by cubic spline interpolation, and then an improved particle swarm optimization algorithm with a random positive feedback factor in velocity updating optimizes the path. The experimental results show that the proposed method can successfully avoid the obstacles and reach the target object. In addition, the inertial positioning strategy and the improvement of particle swarm optimization can effectively shorten the path of the robot.

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“…The global optimal path planning for mobile robots have been a hotspot research area for many years, and several optimization methods such as potential field method [1][2][3], visibility graph method [2] and grid method [3][4][5] have been developed to solve this problem. Although the potential field method is simp1e in structure and is widely used in real-time obstacle avoidance, it has some inherent limitations, including trap situation due to local minima, no passage between closely spaced obstacles, oscillations in the presence of obstacle and in narrow passage.…”

Section: Introductionmentioning

confidence: 99%

Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm

Liang

2009

Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation

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Global path planning for mobile robot using genetic algorithm and simulated annealing algorithm is investigated in this paper. In view of the slow convergence speed of the conventional simulated annealing algorithm, a modified simulated annealing algorithm is presented, and a hybrid algorithm based on the modified simulated annealing algorithm and genetic algorithm is proposed. The proposed algorithm includes three steps: the MAKLINK graph theory is adopted to establish the free space model of mobile robots firstly, then Dijkstra algorithm is utilized for finding a feasible collision-free path and fixing on the sub-searchspace where the global optimal path inside, finally the global optimal path of mobile robots is obtained based on the hybrid algorithm of modified simulated annealing algorithm and genetic algorithm. Experimental results indicate that the proposed algorithm has better performance than simulated annealing algorithm and ant system algorithm in term of both solution quality and computational time, and thus it is a viable approach to mobile robot global path planning.

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Grid modeling of robot cells: A memory-efficient approach

Cited by 25 publications

References 12 publications

A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

A General Framework of Multipopulation Methods With Clustering in Undetectable Dynamic Environments

A cubic spline method combing improved particle swarm optimization for robot path planning in dynamic uncertain environment

Global path planning for mobile robot based genetic algorithm and modified simulated annealing algorithm

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