An adaptive genetic algorithm for robot motion planning in 2D complex environments

Karami, Amir Hossein; Hasanzadeh, Mahdi

doi:10.1016/j.compeleceng.2014.12.014

Cited by 90 publications

(62 citation statements)

References 22 publications

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“…The aim of this study is to ensure delivering people located in the building to the collection area in the 13 disable point (19,18,16,15,13,12,11,10,8,6,4,3 , 1) 30 18 disable point (23,22,21,20,19,18,17,15,14,12,11,8,7,6,5,3, 2, 1) 40 23 disable point (33,31,29,28,27,25,24,23,20,19,18,17,15,14,12,11,10,9,7,5,4, 3, 1) 50 28 disable point (50,49,48,46,...…”

Section: Resultsmentioning

confidence: 99%

“…Their results provide high-quality maps which are same as reference maps with less than 2 m of difference on average. A method based on an adaptive genetic algorithm for robot motion planning is investigated by Karami and Hasanzadeh [11]. They obtain better performance than the other methods in the literature with regard to the quality of the solution and finding an optimum path.…”

Section: Introductionmentioning

confidence: 99%

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A multi-objective route planning model based on genetic algorithm for cuboid surfaces

2018

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During a natural disaster, risk management for the evacuation of people in high-rise buildings is very important for saving lives. In the case of fire, all parameters such as detection, lighting, warning systems, etc. for safety must be used interactively. Determination of evacuation conditions and different ways out are important parameters during the fire. In this study, a system is proposed for evacuating people from building with the shortest/safest route, taking into account certain factors to evaluate the current situation of the fire. Travelling Salesman Problem (TSP) may be adapted to this real-life problem to protect people in the shortest time finding optimum route. In this study, the system based on Genetic Algorithm is performed using the online information about smoke, heat and safety level, the location of fire and the potential congestion of people in order to evacuate people from the building with safety route. The system contains two-and three-dimensional surface applications to ensure evacuation with optimum distance inside/outside of the building. Results are evaluated considering the evacuation distance. Compared to other methods in the literature, the solution to this problem is improved by adding the evacuation process for the elevator and inside of the building.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multi-objective route planning model based on genetic algorithm for cuboid surfaces

2018

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“…The eight paper presents a PSO based algorithms PSO-IAC for obstacle avoidance for manipulator, which can be a part of home service robots [8]. The proposed algorithm for 6 DoF manipulator integrates the constriction factor and the improved adaptive inertia weight with standard PSO.…”

Section: Scanning the Issuementioning

confidence: 99%

Introduction to the Special issue on “Future trends in robotics and autonomous techniques”

Chaudhary

Escolano

Bhojan

2015

Computers & Electrical Engineering

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“…According to the above data, take the pedestrian-bicycle traffic phase T 1 as an example. The adaptive genetic algorithm based on the information entropy [18] is used to extract the characteristic A 3i (i=1,2,3,4,5,6,7,8,9) and B j (i=1,2,3) in the pedestrian-bicycle traffic phase T 1 .…”

Section: Data Processing and Feature Extractionmentioning

confidence: 99%

Cyclist’s Intention Identification on Pedestrian-Bicycle Mixed Sections Based on Phase-Field Coupling Theory

Wang

2019

PROMET

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Bicycle is one of the main factors that affects the traffic safety and capacity on pedestrian-bicycle mixed traffic sections. It is important for implementing the warning of bicycle safety and improving the active safety to identify the cyclists’ intention in the mixed traffic environments under the condition of the “Internet of Things”. The phase-field coupling theory has been developed in this paper to comprehensively analyse the generation, spring up, increase, transfer, regression and reduction method of the traffic phase. The adaptive genetic algorithm based on the information entropy has been used to extract feature vectors of different types of cyclists for intention identification from the reduced pedestrian-bicycle traffic phase, and the theory of evidence has been provided here to build the identification model. The experimental verification shows that the extraction method of cyclists’ intention feature vector and identification model are scientific and reasonable. The theoretical basis can be applied to establishing the pedestrian-bicycle interactive security system.

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An adaptive genetic algorithm for robot motion planning in 2D complex environments

Cited by 90 publications

References 22 publications

A multi-objective route planning model based on genetic algorithm for cuboid surfaces

A multi-objective route planning model based on genetic algorithm for cuboid surfaces

Introduction to the Special issue on “Future trends in robotics and autonomous techniques”

Cyclist’s Intention Identification on Pedestrian-Bicycle Mixed Sections Based on Phase-Field Coupling Theory

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