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<p>This paper introduces a bounded-grid fractional-order chaotic path planner designed for surveillance robots. The key feature of this planner is the mirror mapping technique, which ensures that the robot stays within a predefined grid area during its motion. The chaotic state variable controls the motion angle of the robot in a pseudo-random manner. This paper compares the performance of three fractional-order chaotic systems in this application, Lorenz, Liu, and Chen, and compares them to their integer-order counterparts. The comparison focuses on the resulting robot trajectory and the ratio of the area covered by the robot to the total available working area. The fractional-order systems show higher relative improvement in the ability to cover the area more effectively, suggesting their potential for better performance in surveillance tasks.</p>

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Enhanced robotic path planners utilizing fractional-order chaotic systems

  • 期刊论文

  • 发表日期：

    2025年9月12日

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  • Wafaa Sayed,
  • Menna Elnaggar,
  • Mohamed Walid,
  • Marwan Fetteha,
  • Ahmed Radwan,
  • Lobna Said

摘要

This paper introduces a bounded-grid fractional-order chaotic path planner designed for surveillance robots. The key feature of this planner is the mirror mapping technique, which ensures that the robot stays within a predefined grid area during its motion. The chaotic state variable controls the motion angle of the robot in a pseudo-random manner. This paper compares the performance of three fractional-order chaotic systems in this application, Lorenz, Liu, and Chen, and compares them to their integer-order counterparts. The comparison focuses on the resulting robot trajectory and the ratio of the area covered by the robot to the total available working area. The fractional-order systems show higher relative improvement in the ability to cover the area more effectively, suggesting their potential for better performance in surveillance tasks.