Path planning using Hybrid Routing Algorithm

The aim of this hybrid algorithm is to design and refine a path-finding technique that integrates A * (A-star) and Dijkstra's algorithms, enhancing the efficiency of route planning by utilizing the advantages of both approaches. The method applies the advantages of the two algorithms by adaptin...

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Bibliographic Details
Published in:2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) pp. 1 - 6
Main Authors: G, Abishek, P, Gokul Vasan, N, Janarthanan, P, Kumar
Format: Conference Proceeding
Language:English
Published: IEEE 29.04.2025
Subjects:
A Algorithm
Autonomous Systems
Decision making
Dijkstra's Algorithm
Heuristic algorithms
Hybrid Al-gorithm
Machine learning
Machine learning algorithms
Navigation
Obstacle Avoidance
Path planning
Real-time systems
Robot Navigation
Routing
Technological innovation
Uncertainty
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Summary:The aim of this hybrid algorithm is to design and refine a path-finding technique that integrates A * (A-star) and Dijkstra's algorithms, enhancing the efficiency of route planning by utilizing the advantages of both approaches. The method applies the advantages of the two algorithms by adapting them according to the density of the environment. Dijkstra's algorithm is an optimal search algorithm that tries all possible paths to find the shortest path, but is resource-intensive in complex or large environments. The A *(A-star) algorithm optimizes performance by using heuristics to estimate the cost of reaching the goal, thus focusing on dependency and avoiding unnecessary computations. The hybrid strategy attempts to achieve performance and efficiency by combining A * 's efficiency in sparse setting and Dijkstra's extensive search in dense setting. Thus, it maximizes optimal path-finding while minimizing computational effort. Large-scale simulations were performed in MATLAB to compare the algorithm's performance in different environments with varying challenges. The comparison involves the evaluation of the efficiency, effectiveness, and computational complexity of the hybrid algorithm with respect to the standalone A * algorithm and Dijkstra's algorithm implementations.
DOI:10.1109/AIMLA63829.2025.11040851