Path Planning for Differential Drive Mobile Robot to Avoid Static Obstacles Collision using Modified Crossover Genetic Algorithm

Utami, Nia Saputri; Jazidie, Achmad; Kadier, Rusdhianto Effendi Abdul

doi:10.1109/isitia.2019.8937184

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The simulation consequences showed that average iterations numbers and turns values are optimal but the authors did not give the value of the time and the path length. Utami et al [63] utilized a mutated genetic algorithm method for collision-free paths planning with static obstacles. The consequence of the test showed that it still got a fast time for an ideal solution.…”

Section:  Genetic Algorithm (Ga)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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Mobile robots use is rising every day. Path planning algorithms are needed to make a traveler of robots with the least cost and without collisions. Many techniques have been developed in path planning for mobile robot worldwide, however, the most commonly used techniques are presented here for further study. This essay aims to review various path planning strategies for mobile robots using different optimization methods taken recent publisher’s paper in last five year.

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Section:  Genetic Algorithm (Ga)mentioning

confidence: 99%

A Review on Path Planning Algorithms for Mobile Robots

Abed¹,

Lutfy²,

Al-Doori³

2021

ETJ

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“…Menghindari hambatan juga dibahas pada penelitian yang lain pada pembentukan formasi beberapa agen supaya tidak mengalami benturan antar agennya [3]. Dalam perencanaan jalur robot dengan hambatan statis, perlu perhitungan yang pasti untuk menghindari hambatan sesuai dengan jalur yang telah direncanakan [4]. Untuk mobil robot yang memiliki heading, penelitian ini fokus pada seberapa optimal robot menghindari hambatan dinamis supaya jalur yang ditempuh adalah minimal [5].…”

Section: Pendahuluanunclassified

Prototipe Robot Avoider sebagai Mesin Penggerak Robot Medical Assistant

Fandidarma

Praditya

Kurniawan

2020

ELECTRA

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<em>Permasalahan yang diangkat pada penelitian ini adalah membangun prototipe mesin penggerak Robot Medical Assistant yang memiliki fungsi Obstacle Avoidance dengan gerak otonom. Penelitian yang sudah dilaksanakan sebelumnya masih menggunakan joystik untuk menggerakkan robot atau dengan kata lain robot masih secara manual disetir manusia. Karena sangat tidak reliable dan terlalu bergantung pada manusia, maka metode setir manual ini perlu ditingkatkan performanya dengan pengaplikasian metode otonom pada robot. Maka dalam penelitian ini disuguhkan suatu metode yang lebih baik supaya robot dapat bergerak secara otonom dan memiliki fungsi Obstacle Avoidance supaya robot dapat menghindari halangan dengan baik. Jadi user menentukan titik tujuan dimana saja dan tugasnya apa lalu menekan tombol ‘START’ ketika robot siap berangkat. Pengujian dilakukan dengan memberikan perintah untuk robot bergerak dari satu lokasi ke lokasi lainnya dengan dipasang beberapa halangan pada jalur yang tersedia. Dari pengujian ini mendapatkan hasil bahwa robot lumayan berhasil menghindari hambatan dan pergi ke tujuan dengan rerata kesalahan pengukuran sensor sebesar 1.25%.</em>

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“…According to whether the environmental information changes with time, path planning can be divided into static path planning and dynamic path planning. This paper mainly studies the known static path planning in global environment [7][8][9]. The general steps include environment modeling, path searching, and path optimization.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

Liu

2021

Wireless Communications and Mobile Computing

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Path planning is the core technology of mobile robot decision-making and control and is also a research hotspot in the field of artificial intelligence. Aiming at the problems of slow response speed, long planning path, unsafe factors, and a large number of turns in the conventional path planning algorithm, an improved multiobjective genetic algorithm (IMGA) is proposed to solve static global path planning in this paper. The algorithm uses a heuristic median insertion method to establish the initial population, which improves the feasibility of the initial path and generates a multiobjective fitness function based on three indicators: path length, path security, and path energy consumption, to ensure the quality of the planned path. Then, the selection, crossover, and mutation operators are designed by using the layered method, the single-point crossover method, and the eight-neighborhood-domain single-point mutation method, respectively. Finally, the delete operation is added, to further ensure the efficient operation of the mobile robot. Simulation experiments in the grid environment show that the algorithm can improve the defects of the traditional genetic algorithm (GA), such as slow convergence speed and easy to fall into local optimum. Compared with GA, the optimal path length obtained by planning is shortened by 17%.

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Path Planning for Differential Drive Mobile Robot to Avoid Static Obstacles Collision using Modified Crossover Genetic Algorithm

Cited by 8 publications

References 6 publications

A Review on Path Planning Algorithms for Mobile Robots

A Review on Path Planning Algorithms for Mobile Robots

Prototipe Robot Avoider sebagai Mesin Penggerak Robot Medical Assistant

Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

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