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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| Vydáno v: | 2025 3rd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) s. 1 - 6 |
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IEEE
29.04.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | N, Janarthanan P, Gokul Vasan P, Kumar G, Abishek |
| Author_xml | – sequence: 1 givenname: Abishek surname: G fullname: G, Abishek email: abishek130803@gmail.com organization: KS Rangasamy College of Technology,Department of ECE,Tiruchengode,Tamil Nadu,India – sequence: 2 givenname: Gokul Vasan surname: P fullname: P, Gokul Vasan email: gokulvasan425@gmail.com organization: KS Rangasamy College of Technology,Department of ECE,Tiruchengode,Tamil Nadu,India – sequence: 3 givenname: Janarthanan surname: N fullname: N, Janarthanan email: janajana9047@gmail.com organization: KS Rangasamy College of Technology,Department of ECE,Tiruchengode,Tamil Nadu,India – sequence: 4 givenname: Kumar surname: P fullname: P, Kumar email: kumar@ksrct.ac.in organization: KS Rangasamy College of Technology,Department of ECE,Tiruchengode,Tamil Nadu,India |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Path planning using Hybrid Routing Algorithm |
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