Application of Fuzzy Logic for Collision Avoidance of Mobile Robots in Dynamic-Indoor Environments

Oleiwi, Bashra Kadhim; Mahfuz, Asif; Roth, Hubert

doi:10.1109/icrest51555.2021.9331072

Cited by 18 publications

(14 citation statements)

References 15 publications

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“…Despite the modifications in the first modification, The averaging weight in ( 14) the effect is not sufficient in our implementation, thus suggesting another modification by ( 16) was needed. In classic GWO, the location vector regarding a grey wolf is guided equally through positions of β, α, and δ wolves as it has been presented by (14). In the presented study, a higher value of the weight is given to α wolf, which is (16) in which w𝛽, w𝛼, and w𝛿 represent weight values for 𝛽, 𝛼, and 𝛿 wolves, respectively.…”

Section: B Second Modification: Adaptive Variable Weights (Avw) Approachmentioning

confidence: 99%

“…However, this algorithm is prone to early convergence and cannot avoid local ex-tremity. Oleiwi et al [14] utilized fuzzy logic control for avoiding collisions with dynamic obstacles in the cases of the partially unknown environments, and they utilized A-star technique in order to discover the route off-line. This method is incapable of dealing with completely unknown or maze-like settings.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive weight grey wolf algorithm application on path planning in unknown environments

Abed

Lutfy

Al-Doori

2022

IJEECS

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Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A comparison study result between the proposed method and two other algorithms revealed that the proposed algorithm performed better in avoiding obstacles, which include the situation with the local minima. Finally, when put to comparison with Hybrid Fuzzy-Wind Driven Optimization and Adaptive Particle Swarm Optimization the average improvement rates in route length are 2.86% and 4.70391%, respectively.

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Section: B Second Modification: Adaptive Variable Weights (Avw) Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Abed

Lutfy

Al-Doori

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Several companies around the world, including Amazon, Walmart, DHL, and Zookal, which invested in drone research for a variety of purposes, including freight and package delivery to consumers, have lately revealed the use of drones for commercial purpose [3]. One of the primary aspects of the robot's transition from one location to another is to generate a path plan from the starting point to the target point and avoid collisions with obstacles [1][2][3][4]. The global path planner is one vehicle path planning system type, it uses a priori information from the road map to generate the optimal possible path from the starting point to the destination point [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Planning the Optimal 3D Quadcopter Trajectory Using a Delivery System-Based Hybrid Algorithm

2023

IJIES

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Path planning is one of the most crucial aspects of implementing unmanned aerial vehicle (UAV) missions. Therefore, it is essential to figure out the optimal path from the starting point to the target point in different scenarios. In this paper, we propose employing a hybrid algorithm, named FPA-GA, generated by combining the flower pollination algorithm (FPA) and genetic algorithm (GA), to find an optimum path in a real modern building's environment. In addition, a cubic polynomial algorithm via two points was proposed to make the route adequate and smooth. Because the GA has a good capability for exploring the search space, it is employed for exploration while the FPA and GA are employed to increase the exploitation capability in the proposed algorithm. Five different scenarios are utilized to evaluate FPA-GA's ability to find the optimal path in a variety of situations, and then the proposed algorithm's performance is compared with that of seven other algorithms: GA, FPA, bat algorithm (BA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved whale optimization algorithm (IWOA), and IWOA-PSO. The best path, the mean path length, the standard derivation, and the worst path length are the four parameters used to compress data. In all scenarios, the results demonstrate that FPA-GA is capable of locating the shortest and most collision-free paths, and the hybrid algorithm FPA-GA is always superior to other algorithms. The proposed algorithm provided the mean path length enhancement in all scenarios, where the maximum enhancement equals (33.6%), (23.1%), (55.5%), (29.8%), (50.5%), (53.9%), and (26.4%) compared with GA, FPA, BA, PSO, WOA, IWOA, and IWOA-PSO, respectively. On the other hand, the proposed algorithm is guaranteed to find the best path in all scenarios, where the standard deviation of FPA-GA is always less than that of other algorithms.

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“…However, the algorithm does not guarantee a collision-free path in crowded environments. Oleiwi et al [13] applied fuzzy logic control for collision avoidance using only dynamic obstacles in partially unknown and known environments and they used the A-star algorithm to find the route in an offline manner. The drawback of this method is that it cannot work in fully unknown or maze environments.…”

Section: Introductionmentioning

confidence: 99%

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Abed¹,

Lutfy²,

Al-Doori³

2022

JESA

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Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.

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Application of Fuzzy Logic for Collision Avoidance of Mobile Robots in Dynamic-Indoor Environments

Cited by 18 publications

References 15 publications

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Planning the Optimal 3D Quadcopter Trajectory Using a Delivery System-Based Hybrid Algorithm

Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

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