Solving robot path planning problem by using a new elitist multi-objective IWD algorithm based on coefficient of variation

Salmanpour, Soheila; Monfared, Hesamoddin; Omranpour, Hesam

doi:10.1007/s00500-015-1991-z

Cited by 25 publications

(8 citation statements)

References 20 publications

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“…Using multi-objective fuzzy optimization strategies and Voronoi diagrams to coordinate weight conflicts of sub-objective functions [19], developed a new approach to improve the cost performance index. The Pareto front was calculated using the intelligent water drops (IWD) algorithm, a multi-objective elitist approach based on coefficients of variation that have been proposed in [20]. The method is called CV-based MO-IWD, and it is aimed at optimizing both the length of the path as well as the safety of the path.…”

Section: Related Workmentioning

confidence: 99%

Hybrid approach for multi-objective optimization path planning with moving target

Abed

Jasim

2022

IJEECS

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Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.

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Section: Related Workmentioning

confidence: 99%

Hybrid approach for multi-objective optimization path planning with moving target

Abed

Jasim

2022

IJEECS

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“…The multi-objective intelligent water drops (IWD) algorithm was designed for the path planning problem in ref. [40]. The path length and safety were taken into account.…”

Section: Related Workmentioning

confidence: 99%

Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

Yang

Sun

2018

Applied Sciences

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Path planning problems involve finding a feasible path from the starting point to the target point. In mobile robotics, path planning (PP) is one of the most researched subjects at present. Since the path planning problem is an NP-hard problem, it can be solved by multi-objective evolutionary algorithms (MOEAs). In this article, we propose a multi-objective method for solving the path planning problem. It is a population evolutionary algorithm and solves three different objectives (path length, safety, and smoothness) to acquire precise and effective solutions. In addition, five scenarios and another existing method are used to test the proposed algorithm. The results show the advantages of the algorithm. In particular, different quality metrics are used to assess the obtained results. In the end, the research indicates that the proposed multi-objective evolutionary algorithm is a good choice for solving the path planning problem.

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“…Hosseini 7,8 has used the IWD algorithm to extract a solution for an n-queen puzzle and TSP problem. Salmanpour et al 9,10 used a generalized IWD algorithm with fuzzy interface for path tracking problem of robots. Bansal et al 11 have used an IWD algorithm to decode the graph-based TSP problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

et al. 2021

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SUMMARY In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.

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Solving robot path planning problem by using a new elitist multi-objective IWD algorithm based on coefficient of variation

Cited by 25 publications

References 20 publications

Hybrid approach for multi-objective optimization path planning with moving target

Hybrid approach for multi-objective optimization path planning with moving target

Solving the Path Planning Problem in Mobile Robotics with the Multi-Objective Evolutionary Algorithm

Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

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