Global path planning for mobile robots in large-scale grid environments using genetic algorithms

Alajlan, Maram; Koubâa, Anis; Châari, Imen; Bennaceur, Hachémi; Ammar, Adel

doi:10.1109/icbr.2013.6729271

Cited by 54 publications

(33 citation statements)

References 14 publications

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“…[3] Mobile Robot A solution to path planning problem and environment modeling. [31] An optimized path planning method for solve path planning problem. [32] PSO Blind people A path planning approach with predetermined waypoints for blind people.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Mohamed¹,

E.²,

F.³

2019

IJCA

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It is a challenging task for the blind people to perceive indoor environment information and walk independently. Path planning is a crucial requirement to provide constant assistance for blind people to navigate from one location to another. This paper presents a novel system for path planning to assist blind people for walking through the indoor environment and avoiding the obstacles. The algorithm is based on genetic algorithm and A* algorithm to select the shortest path that achieves the lowest computational time. A comparison a mong the GA, A* and the new algorithm shows that is faster and shorter than the previous algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Through the grid-based representation [52] using a grid that is orderly numbered. In [53], the grid map has been employed, where the environment has been segmented into cells with the same size.…”

Section: Indoor Environment Representationmentioning

confidence: 99%

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Mohamed¹,

E.²,

F.³

2019

IJCA

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“…The grid-map is divided into cells in which the robot has to traverse a set of free cells to reach its goal. In the literature, several heuristic solutions and approaches have been proposed to solve the global path planning problem such as Ant Colony Optimization (ACO) (Chaari et al 2012), Genetic Algorithms (GA) (Alajlan et al 2013), Particle Swarm Optimization (PSO) (Shiltagh and Jalal 2013) and Tabu Search (TS) (Masehian and Amin-Naseri 2006). Other techniques are based on exact methods, such as Dijkstra and A*, which have the advantage of being complete and guarantee finding the optimal solution if it exists.…”

Section: Introductionmentioning

confidence: 99%

“…We are addressing this research question in the iroboapp project (Iroboapp 2015a), aiming at understanding the capabilities and performance of existing approaches for solving the global path planning problem and design new hybrid algorithms for efficient path search in large-scale environments. In previous papers (Alajlan et al 2013;Chaari et al 2012), we have investigated heuristic methods including GA, Tabu Search and ACO for solving the global path planning problem. In this paper, we focus on exact methods, namely Dijkstra and A* algorithms, to design new fast and exact algorithms for the global path planning problem in large-scale grid environments.…”

Section: Introductionmentioning

confidence: 99%

Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments

et al. 2015

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Although there exist efficient methods to determine an optimal path in a graph, such as Dijkstra and A* algorithms, large instances of the path planning problem need more adequate and efficient techniques to obtain solutions in reasonable time. We propose two new time-linear relaxed versions of Dijkstra (RD) and A* (RA*) algorithms to solve the global path planning problem in large grid environments. The core idea consists in exploiting the grid-map structure to establish an accurate approximation of the optimal path, Communicated by V. Loia. B Imen Châari without visiting any cell more than once. We conducted extensive simulations (1290 runs on 43 maps of various types) for the proposed algorithms, both in four-neighbor and eight-neighbor grid environments, and compared them against original Dijkstra and A* algorithms with different heuristics. We demonstrate that our relaxed versions exhibit a substantial gain in terms of computational time (more than 3 times faster in average), and in most of tested problems an optimal solution (in at least 97 % of cases for RD and 82 % for RA*) or a very close one is reached (at most 9 % of extra length, and less than 2 % in average). Besides, the simulations also show that RA* provides a better trade-off between solution quality and execution time than previous bounded relaxations of A* that exist in the literature.

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“…Klasik yöntemler; Hücre Ayrışımı yöntemi (Cell Decomposition-CD) [4,5], Potansiyel Alan yöntemi (Potential Field-PFM) [6,7,8], Alt Hedef Ağı yöntemi (Subgoal Method-SG) [9,10,11], Örnekleme Tabanlı yöntemler (Sampling-based methods-SBP) [12] kendi arasında Rastgele Ağaçlar yöntemi (Rapidly Exploring Random Trees-RRT) ve Olasılıksal Yol Haritası yöntemi (Probabilistic Road Map-PRM) olarak karşımıza çıkar. Sezgisel yöntemlerden, birden fazla değişken komşuluk araştırması [13], Yapay Sinir Ağları (Neural Network-NN) yöntemi [14,15,16] , Bulanık Mantık (Fuzzy Logic-FL) [17,18] ,Karar Destek Makinaları [19] , Doğadan Esinlenen algoritmalar; Genetik algoritması (Genetic Algorithms-GA) [20,21,22,23],Yarasa algoritması (Bat algorithm) [24], Parçacık Sürüsü optimizasyonu (Particle Swarm Optimization-PSO) [25,26], Karınca Kolonisi optimizasyonu (Ant Colony Optimization-ACO) [27], Yapay Arı Kolonisi Algoritması (Artifical Bee Colony-ABC ) [28] ve bunların melez algoritma çalışmaları yapılmıştır.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Optimum Enerji Verimliliğini Hedefleyen Rastgele Ağaçlar ve Yapay Arı Kolonisi Yöntemi ile Otonom Robotlarda Yol Planlama Algoritması

Torun

Ergül

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Global path planning for mobile robots in large-scale grid environments using genetic algorithms

Cited by 54 publications

References 14 publications

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

A Novel Path Planning System for Guidance of Blind People using Integration of GA and A* Techniques

Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments

Optimum Enerji Verimliliğini Hedefleyen Rastgele Ağaçlar ve Yapay Arı Kolonisi Yöntemi ile Otonom Robotlarda Yol Planlama Algoritması

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