Safe and Optimum Navigation of Wheeled Mobile Robot using Grey Wolf Optimization Algorithm

Abaas, Tahseen Fadhel; Shabeeb, Alaa Hassan

doi:10.1088/1757-899x/928/2/022006

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…In the GWO code, there are no omega wolves. The following is a mathematical model of the grey wolf hunting mechanism [13]:…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

Abaas¹,

Shabeeb²

2022

ETJ

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 The proposed hybrid algorithm outperforms the PSO, IPSO, and GWO algorithms.  The proposed method outperforms class PSO and GWO algorithms in determining the shortest and collision-free path for a mobile robot under the same environmental restrictions.  The performance made the hybrid algorithm more effective in finding the best potential solution.In the mobile robot workplace, the path planning problem is crucial. Robotic systems employ intelligence algorithms to plan the robot's path from one point to another. This paper proposes the fastest and optimal path planning of the wheeled mobile robot with collision avoidance to find the optimal route during wheeled mobile robot navigation from the start point to the target point. It is done using a modern meta-heuristic hybrid algorithm called IPSOGWO by combining Improved Particle Swarm Optimization (IPSO) with Grey Wolf Optimizer (GWO). The principal idea is based on boosting the ability to exploit in PSO with the exploration ability in GWO to the better-automated alignment between local and global search capabilities towards a targeted, optimized solution. The proposed hybrid algorithm tackles two objectives: the protection of the path and the length of the path. During, Simulation tests of the route planning by the hybrid algorithm are compared with individual results PSO, IPSO, and GWO concepts about the minimum length of the path, execution time, and the minimum number of iterations required to achieve the best route. This work's effective proposed navigation algorithm was evaluated in a MATLAB environment. The simulation results indicated that the developed algorithm reduced the average path length and the average computation time, less than PSO by (1%, 1.7%), less than GWO by (1%, 1.9%), and less than IPSO by (0.05%, 0.4%), respectively. Furthermore, the superiority of the proposed algorithm was proved through comparisons with other famous path planning algorithms with different static environments.

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“…In the GWO code, there are no omega wolves. The following is a mathematical model of the grey wolf hunting mechanism [13]:…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

Abaas¹,

Shabeeb²

2022

ETJ

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“…In the GWO code, there are no omega wolves. The following is a mathematical model of the grey wolf hunting mechanism [15]:…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Path Planning and Obstacle Avoidance of a Mobile Robot based on GWO Algorithm

Abbas

Shabeeb

2022

alkej

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تعد مشكلة تخطيط المسار من بين المشكلات الأكثر أهمية في مجال أبحاث الروبوتات. نظرًا لأنه مرتبط بإيجاد طريق آمن وفعال في بيئة مزدحمة للروبوتات المتنقلة ذات العجلات ، ويعتبر شرطًا أساسيًا مهمًا لنجاح أي مشروع روبوت متنقل. تقترح هذه الورقة التخطيط الأسرع والأمثل لمسار الروبوت المتحرك ذي العجلات مع تجنب الاصطدام باستخدام خوارزمية تسمى تحسين الذئب الرمادي (GWO) كطريقة للعثور على أقصر الطرق وأكثرها أمانًا. تهدف أهداف البحث في هذه الدراسة إلى تحديد أفضل مسار ملاحي مع مراعاة تأثير عدد المعوقات والتكرارات على أداء الخوارزمية لإيجاد المسار الأفضل. أثبتت المحاكاة نجاح الطريقة المقترحة في إيجاد الطريق الآمن والأقصر. أظهر تقييم الخوارزمية المقترحة وجود علاقة بين زيادة عدد العوائق وعدد التكرارات كمعلمات تأثير على طول المسار والوقت المطلوب لإيجاد المسار الأمثل.

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“…[26].to determine the best operating strategy for economic load dispatch (ELD) [27]. to solve the challenge of robot path planning to find the shortest path [28]. In WSNs, used for cluster head selection [29].…”

Section: Introductionmentioning

confidence: 99%

Solving Suggested Problems using Grey Wolf Optimization

Issa¹,

Alaa²,

Abed³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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In this research, the Grey Wolf Optimizer (GWO) algorithm was applied, which is a type of metaheuristic optimization algorithms that is important for identifying problems and finding ways to improve them. In 2014, Seyedali Mirjalili came up with this algorithm. Its social hierarchy is modeled after that of grey wolves in nature. These wolves live in groups consisting of( 5 -12) individuals. Wolves are divided into four levels, alpha represents the first level it is accountable for the manufacture of important resolutions for the peak like hunting, bedtime, wake up, etc. As for the second level in the hierarchy, it represents the beta wolf and is the advisor of the alpha. Beta can be the leader after the death of one of the alpha wolves. The third level represents delta, which follows the commands of alpha and beta. it is the dominant omega. omega represents the last level, which obeys all other wolves. Furthermore, the chief algorithm stages such as chasing, searching the prey, encircle and attack the prey were applied. GWO algorithm was tested on three benchmarks test functions using MATLAB R2014a. the results were confirmed by comparing GWO through another intelligent swarm algorithm like Particle Swarm Optimization (PSO) algorithm. results showed the superiority GWO in achieving better results and high convergence speed.

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Safe and Optimum Navigation of Wheeled Mobile Robot using Grey Wolf Optimization Algorithm

Cited by 4 publications

References 8 publications

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

Obstacle Avoidance and Path Planning of a Wheeled Mobile Robot Using Hybrid Algorithm

Path Planning and Obstacle Avoidance of a Mobile Robot based on GWO Algorithm

Solving Suggested Problems using Grey Wolf Optimization

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