MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots

Subramanian, Sakthitharan; Rajesh, Sudha; Britto, Preethika Immaculate; Sankaran, Sindhuja

doi:10.1080/01969722.2023.2177804

Cited by 2 publications

(1 citation statement)

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“…Examples of such robots include robotic vacuum cleaners, adaptive navigation systems [2][3], mobile robots [4] [5], and more. This capability enables robots to be more widely used in everyday life, industry [6] [7], healthcare, and aviation and maritime sectors [8], enhancing productivity, reducing…”

Section: Introductionmentioning

confidence: 99%

Algorithm Selection and Application for Robot Path Planning Problems

Chen,

et al. 2024

J. Phys.: Conf. Ser.

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This article primarily delves into the investigation of robotic path planning in the presence of obstacles, aiming to ascertain optimal traversal routes. It conducts a thorough categorization and discussion of two critical aspects: the ascertainability of the overall map and the regularity of obstacles. In addressing this inquiry, two obstacle scenarios, namely regular and irregular, are posited. The article scrutinizes the path planning and obstacle circumvention techniques for both scenarios. Regarding regular obstacles, a comparative assessment of the A* algorithm, Floyd’s algorithm, and Dijkstra’s algorithm was conducted, culminating in the selection of the A* algorithm for its superior efficiency. For irregular obstacles, the article proposes a pre-processing approach involving the utilization of Matlab’s iterative pixel point traversal to assess obstacle proportions within nodes, subsequently converting irregular obstacles into a regularized format. To summarize, in scenarios with a known overall map, the article advocates employing the A* algorithm for proficient path planning. In situations where the map is undisclosed but obstacles exhibit regularity, the D* algorithm is recommended. For instances involving irregular obstacles and an undisclosed map, a dynamic method for handling newly incorporated nodes into the map is proposed. This article presents tailored solutions for robotic path planning across diverse conditions, offering concise yet effective problem-solving strategies. In conclusion, this study offers a comprehensive array of solutions for robot path planning in obstructed environments, enabling the selection of apt methodologies based on varying conditions and exigencies.

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