Robot Path Planning Based on Hybrid Adaptive Dimensionality Representation with Glowworm Swarm Optimization

Mahmood, Qasim Radam; Hasan, Ali; Khafaji, Hussein K.

doi:10.1109/iiceta51758.2021.9717626

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…To improve path planning, the proper optimization method must be used [3][4][5]. Therefore, researchers have proposed many path planning methods concerning this problem, including the grid method [6], D* algorithm [7], and some intelligent methods, like particle swarm optimization (PSO) [8], Ant Colony Optimization (ACO) [9], artificial bee colony (ABC) [10], and Glowworm Swarm Optimization (GOS) [11]. To find safe ways with minimal energy consumption, Mansoor Davoodi et al [12] proposed two multi-objective planning models.…”

Section: Introductionmentioning

confidence: 99%

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

Hasan

Abdullah

Saleh

2023

Int. J. Interact. Mob. Technol.

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— This paper proposes a new obstacle avoidance method for finding an optimal path passing through wide free space based on the optimized MAX-MIN Ant System (MMAS) algorithm in dynamic environments. The proposed improvement of the MMAS algorithm occurs in two stages. The first stage analyses the environment with two modifications by proposing a new relation of pheromone trail updating to construct the consequence modified (deposited) pheromone trail update in each iteration based on adding a new parameter (clean) to calculate the possible wide empty locations. Another alteration at this level involves checking and identifying the four horizontal and vertical states. Suppose the two nodes on either side of the four diagonal nodes are barriers in the next neighbor locations. In that case, the Tight Tunnel state is then attained, and it is managed by sealing this location to prevent passing through it before the robot movement stage. Another alteration is made to the robot-moving stage by recommending a new relation of tour building probabilities to determine the best option for moving the robot from the start node through a dynamic environment that incorporates dynamic obstacles moving through free space by locating and exhibiting the ideal path via broad free space. The main outcome of the experiment simulation of the new method involves that the robot avoids going through narrow tunnels and chooses the best course through open areas to reach its destination without running into any impediments in various dynamic environments conditions. In addition, the comparison of average time occupied to find the paths by the new method reduces to 54.32% as compared with that required with the method of hybrid the basic D* algorithm with the Lbest PSO algorithm, and to 11.95% in comparison to the time occupied recorded when applying the method of the improved MAX–MIN ACO algorithm.

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Section: Introductionmentioning

confidence: 99%

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

Hasan

Abdullah

Saleh

2023

Int. J. Interact. Mob. Technol.

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“…Kamil [10] suggested a modified version of the artificial Bee Colony Algorithm (ABC), namely the Adaptive Dimension Limit-Artificial Bee Colony Algorithm (ADL-ABC), to determine the optimum global path for the mobile robot algorithm, which had a great potential to solve the proposed problem, the proposed algorithm performed better than the ABC algorithm. Mahmood [11] illustrated a creative method for path planning. The improved approach is created using the glowworm swarm optimization technique and adaptive dimensionality representation.…”

Section: Introductionmentioning

confidence: 99%

A Constraint Optimization path planning for Dynamic Environment

Khalil,

Abdullah,

Hassnawi

2023

ITM Web Conf.

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Optimal path planning is a major challenge for the operation of autonomous unmanned ground vehicles. In dynamic mobile path planning, the goal is to find a collision-free path for a robot from an initial position to a goal position in an environment with obstacles. However, in a dynamic environment, the challenge is bigger than static. In this paper, a modified ABC and an adaptive constraint PSO algorithm are proposed to deal with the dynamic environment; the algorithms were chosen to have a good balance between exploration and exploitation, with a penalty function used to have a collision-free path.

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Multi-Agent Swarm Robotics for Accurate Position Detection in Disaster Scenarios

Ingale¹,

Deshmukh²,

Deshpande³

et al. 2023

2023 International Conference on Inventive Computation Technologies (ICICT)

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Robot Path Planning Based on Hybrid Adaptive Dimensionality Representation with Glowworm Swarm Optimization

Cited by 3 publications

References 8 publications

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

A Constraint Optimization path planning for Dynamic Environment

Multi-Agent Swarm Robotics for Accurate Position Detection in Disaster Scenarios

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