Robot Navigation Based on Differential Evolution

Martinez-Soltero, Erasmo Gabriel; Hernández-Barragán, Jesús

doi:10.1016/j.ifacol.2018.07.303

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…In [35], a method is proposed to find the point local positions of a trajectory sequentially (points that the robot travels from an initial location to a final one) through the Differential Evolution (DE) algorithm. The method finds a new feasible point position on each iteration to minimize the distance to an end point.…”

Section: Optimization In Path-planningmentioning

confidence: 99%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

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Section: Optimization In Path-planningmentioning

confidence: 99%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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“…Moreover, the HHO algorithm shows superior performance in real-world engineering problems, including three-bar truss design problem, tension spring design, pressure vessel design problem, welded beam design problem, and multi-plate disc clutch brake [31]. On the other hand, DE is a classical and powerful algorithm, which has been used in order management [32], robot navigation [33], synthetic inertia control [34], and other fields. In addition, DE has also became more and more popular in hybrid algorithms for image segmentation in recent years, and an array of experiments significantly verify its advantages.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

2019

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Multilevel thresholding has got more attention in recent years with various successful applications. However, the implementation becomes more and more complex and time-consuming when the number of thresholds is high, and color images which contain more information are even worse. Therefore, this paper proposes an alternative hybrid algorithm for color image segmentation, the advantages of which lie in extracting the best features from the high performance of two algorithms and overcoming the limitations of each algorithm to some extent. Two techniques, Otsu's method, and Kapur's entropy, are used as fitness function to determine the segmentation threshold values. Harris hawks optimization (HHO) is a novel and general-purpose algorithm, and the hybridization of HHO is fulfilled by adding another powerful algorithm-differential evolution (DE), which is known as HHO-DE. More specifically, the whole population is divided into two equal subpopulations which will be assigned to HHO and DE algorithms, respectively. Then both algorithms operate in parallel to update the positions of each subpopulation during the iterative process. In order to fully demonstrate the superior performance of HHO-DE, the proposed method is compared with the seven state-of-the-art algorithms by an array of experiments on ten benchmark images. Meanwhile, five measures, including the average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM), are used to evaluate the performance of each algorithm. In addition, Wilcoxon's rank sum test for statistical analysis and the comparison with the super-pixel method are also conducted to verify the superiority of HHO-DE. The experimental results reveal that the proposed method significantly outperforms other algorithms. Hence, the HHO-DE algorithm is a remarkable and promising tool for multilevel thresholding color image segmentation.

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“…Since the original publication of DE [28], the method was extended in many ways by, e.g., using adaptive local search [19], modifying the differential mutation operator [35] or optimizing parameters of DE [27]. DE has been also applied to many real-life problem, such as, digital filter design [14], parameter estimation in ODEs [34], discovering predictive genes in microarray data [30], or robot navigation [18].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution with Reversible Linear Transformations

Tomczak¹,

Węglarz-Tomczak²,

Eiben³

2020

Preprint

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Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformation applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.

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Robot Navigation Based on Differential Evolution

Cited by 14 publications

References 11 publications

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

A Novel Hybrid Harris Hawks Optimization for Color Image Multilevel Thresholding Segmentation

Differential Evolution with Reversible Linear Transformations

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