Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets

Sarkar, Ritam; Barman, Debaditya; Chowdhury, Nirmalya

doi:10.1016/j.jksuci.2020.10.010

Cited by 41 publications

(32 citation statements)

References 30 publications

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“…Genetic algorithms are stochastic global search optimization methods that simulate the phenomena of replication, crossover, and variation that occur in natural selection and inheritance. Starting from an initial population, the population evolves to increasingly better regions in the search space by random selection, crossover and mutation operations to produce a group of individuals better suited to the environment, and finally converges to a group of individuals best suited to the environment to obtain a quality solution to the problem ( Nazarahari et al, 2019 ; Sarkar et al, 2020 ). For path planning based on genetic algorithm, the individuals suitable for the environment are the suitable moving paths, and the one that best satisfies the conditions is obtained by random selection and crossover variation.…”

Section: Methodsmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

102

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Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

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Section: Methodsmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

102

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“…Heuristic optimization algorithms offer solutions close to nonlinear mathematics and real life problems. In the literature, heuristic optimization algorithms have been applied in many different areas [83][84][85][86][87]. Particle Swarm Optimization (PSO), one of these algorithms, is a heuristic algorithm put forward by Kennedy and Eberhart in 1995 [88].…”

Section: Pso and Bpsomentioning

confidence: 99%

BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için Derin Özellik Seçimi

et al. 2023

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“…Because GA is widely used in path planning [144], the following year's study included many combinations, improvements, and modifications of this algorithm to further improve AGV's navigation ability. For example, Jiang et al [145] proposed an improved adaptive GA combining with simulated annealing in their work for AGV path planning to achieve strong ability to avoid local optima and faster convergence speed.…”

Section: Genetic Algorithmmentioning

confidence: 99%

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

2023

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Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research.

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Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets

Cited by 41 publications

References 30 publications

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

BPSO ve SVM'ye Dayalı Yüzde Duygu Tanıma için Derin Özellik Seçimi

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

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