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Path planning is a non-deterministic polynomial-time rigid problem. This research compares three distinct trajectory control and path planning algorithms: the spar-row search algorithm (SSA), the hybrid ant colony optimization and genetic algorithm (ACO-GA), and the ant colony optimization (ACO) method. We talk about the best algorithm for a static or dynamic environment. Gaining further insight into how metaheuristic algorithms work when resolving shortest path problems is the aim of this study. In order to tabulate and discuss the results, the convergence curve is plotted and a pixmap is created. The results showed that the SSA had a path time that was 0.07 s faster than the ACO and 0.58 s faster than the ACO-GA. The length of the SSA-generated trajectory optimization algorithm is the overall shortest and smoothest. Moreover, SSA had the maximum path value, the lowest path time, and the fewest iterations—less than 35. For SSA, the angle of rotation worked best since it could determine the destination with the greatest amount of efficiency. Therefore, SSA algorithm yields better results compared to the other two algorithms.

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Path Planning and Trajectory Control of Autonomous Robot Using Metaheuristic Algorithms

  • 会议论文

  • 发表日期：

    2025年

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  • K. Gajendra,
  • K. Thivagar,
  • Vishnu Vardhana Reddy Karna

摘要

Path planning is a non-deterministic polynomial-time rigid problem. This research compares three distinct trajectory control and path planning algorithms: the spar-row search algorithm (SSA), the hybrid ant colony optimization and genetic algorithm (ACO-GA), and the ant colony optimization (ACO) method. We talk about the best algorithm for a static or dynamic environment. Gaining further insight into how metaheuristic algorithms work when resolving shortest path problems is the aim of this study. In order to tabulate and discuss the results, the convergence curve is plotted and a pixmap is created. The results showed that the SSA had a path time that was 0.07 s faster than the ACO and 0.58 s faster than the ACO-GA. The length of the SSA-generated trajectory optimization algorithm is the overall shortest and smoothest. Moreover, SSA had the maximum path value, the lowest path time, and the fewest iterations—less than 35. For SSA, the angle of rotation worked best since it could determine the destination with the greatest amount of efficiency. Therefore, SSA algorithm yields better results compared to the other two algorithms.