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Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm

Journal of Robotics and Control (JRC) · Vol. 6 No. 2 (2025) · hlm. 543–552

DOI
10.18196/jrc.v6i2.25586
ISSN
2715-5072, 2715-5056
Terbit
2025-03-10
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Thorough path planning is critical in unmanned ground vehicle control to reduce path length, computational time, and the number of collisions. This paper aims to introduce a new metaheuristic method called the Hybrid Crocodile Hunting-SearcH and Falcon Optimization (CHS-FO) algorithm. This method combines CHS's exploration and exploitation abilities with FO's rapid convergence rate. In this way, the use of both metaheuristic techniques limits the disadvantage of the individual approach, guaranteeing a high level of both global and local search. We conduct several simulations to compare the performance of the CHS-FO algorithm with conventional algorithms such as A* and Genetic Algorithms (GA). It is found The results show that the CHS-FO algorithm performs 30–50% better in terms of computation time, involves shorter path planning, and improves obstacle avoidance. Eristic also suggests that the path generation algorithm can adapt to environmental constraints and be used in real-world scenarios, such as automating product movement in a warehouse or conducting search and rescue operations for lost vehicles. The primary The proposed CHS-FO architecture makes the robot more independent and better at making choices, which makes it a good choice for developing the next generation of mobile robotic platforms. Goals will encompass the improvement of the algorithm's scalability for use in multiple robots, as well as the integration of the algorithm in a real environment in real time.

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  • University of Basrah
  • Southern Technical University
  • Shatt Al-Arab University College
  • University of Jordan
  • King Faisal University
  • University of Warith Al-Anbiyaa
  • University of Kerbala
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Rekan penulisOrganisasiArtikel bersama
‪Mahmood A. Al-Shareeda‬Southern Technical University18
Zainab Marid AlzamiliAfiliasi tidak diketahui8
Mohammed Amin AlmaiahUniversity of Jordan7
Faris K. AL-ShammriUniversity of Warith Al-Anbiyaa6
Jalal Mohammed Hachim AltmemiSouthern Technical University4
Mansour ObeidatAfiliasi tidak diketahui4
Abdulnasser AbdulJabbar AbboodAfiliasi tidak diketahui4
Md Asri NgadiUniversity of Technology Malaysia4
Abdulaziz Zaid A. AljarwanUniversity of Ha'il4
Murtaja Ali SaareUniversity of Basrah4
Ali A. AlaidanyShatt Al-Arab University College3
Abbas Jalal KaisheshUniversity of Kerbala3
Ghazi Jalal KasheshUniversity of Kerbala3
Hussein Ahmed AliUniversity of Kirkuk2
Saima Anwar LashariSaudi Electronic University2
Saad Abdulhussein AbbasUniversity of Basrah2
Nabil DerbelUniversity of Sfax1
Jawad Hamad HameedUniversity of Gabès1
Marwa K. FarhanMinistry of Higher Education and Scientific Research1
Rommel Al AliAfiliasi tidak diketahui1
Saadaldeen Rashid AhmedAl-Ayen Iraqi University1
Nada Mohammed MuradMinistry of Higher Education and Scientific Research1
May Adnan FalehSouthern Technical University1
Ali M. AbdulsadaUniversity of Warith Al-Anbiyaa1
Mohamed Abdulrahman AbdulhamedUniversity of Basrah1
Mustafa Moosa QasimUniversity of Basrah1
Akram Hussain Abd AliUniversity of Basrah1
Bushra Abdullah ShtaytSouthern Technical University1
Karrar Ali AbdullahShatt Al-Arab University College1
Ali K. MattarShatt Al-Arab University College1
Khansaa Azeez Obayes Al-HusseiniAl-Furat Al-Awsat Technical University1
Ans Ibrahim MahameedUniversity of Tikrit1
Karrar Oun MohanAfiliasi tidak diketahui1
Fatima Sameer HassanAl-Zahraa University for Women1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm</dc:title>
	<dc:creator>Hashim, Wassan Adnan</dc:creator>
	<dc:creator>Ahmed, Saadaldeen Rashid</dc:creator>
	<dc:creator>Mahmood, Mohammed Thakir</dc:creator>
	<dc:creator>Almaiah, Mohammed Amin</dc:creator>
	<dc:creator>Shehab, Rami</dc:creator>
	<dc:creator>AlAli, Rommel</dc:creator>
	<dc:subject xml:lang="en-US">Path Planning</dc:subject>
	<dc:subject xml:lang="en-US">Hybrid Algorithm</dc:subject>
	<dc:subject xml:lang="en-US">Crocodile Hunting Search (CHS)</dc:subject>
	<dc:subject xml:lang="en-US">Falcon Optimization (FO)</dc:subject>
	<dc:subject xml:lang="en-US">Obstacle Avoidance</dc:subject>
	<dc:description xml:lang="en-US">Thorough path planning is critical in unmanned ground vehicle control to reduce path length, computational time, and the number of collisions. This paper aims to introduce a new metaheuristic method called the Hybrid Crocodile Hunting-SearcH and Falcon Optimization (CHS-FO) algorithm. This method combines CHS's exploration and exploitation abilities with FO's rapid convergence rate. In this way, the use of both metaheuristic techniques limits the disadvantage of the individual approach, guaranteeing a high level of both global and local search. We conduct several simulations to compare the performance of the CHS-FO algorithm with conventional algorithms such as A* and Genetic Algorithms (GA). It is found The results show that the CHS-FO algorithm performs 30–50% better in terms of computation time, involves shorter path planning, and improves obstacle avoidance. Eristic also suggests that the path generation algorithm can adapt to environmental constraints and be used in real-world scenarios, such as automating product movement in a warehouse or conducting search and rescue operations for lost vehicles. The primary The proposed CHS-FO architecture makes the robot more independent and better at making choices, which makes it a good choice for developing the next generation of mobile robotic platforms. Goals will encompass the improvement of the algorithm's scalability for use in multiple robots, as well as the integration of the algorithm in a real environment in real time.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-03-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/25586</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i2.25586</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 2 (2025); 543-552</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/25586/11369</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Wassan Adnan Hashim, Saadaldeen Rashid Ahmed, Mohammed Thakir Mahmood, Mohammed Amin Almaiah, Rami Shehab, Rommel AlAli</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>