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Enhanced Robot Motion Block of A-Star Algorithm for Robotic Path Planning.

Raihan Kabir1, Yutaka Watanobe1, Md Rashedul Islam2

  • 1Department of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu 965-8580, Japan.

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Summary
This summary is machine-generated.

This study introduces an optimized robot path-planning algorithm using an adaptive cost function to enhance the A-star (A*) algorithm. The new method significantly reduces search nodes and time complexity for efficient robot navigation.

Keywords:
A* algorithmBFSDFSDijkstraGazebo simulatorROSSLAMTWA*adaptive cost functionpath planningrobot motion block (RMB)

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • Robot path-planning is crucial for autonomous navigation.
  • Conventional A-star (A*) algorithm has limitations in time, space, and search nodes.
  • Robot motion block (RMB) optimization is key to improving path-planning efficiency.

Purpose of the Study:

  • To propose an optimized robot motion block (RMB) with an adaptive cost function.
  • To enhance the performance of the A-star (A*) algorithm for robot path-planning.
  • To reduce the number of search nodes and time complexity in path-planning.

Main Methods:

  • Developed an adaptive cost function that tracks the goal node and adjusts movement costs.
  • Integrated the adaptive cost function with an optimal RMB.
  • Conducted extensive experiments using an open-source dataset with varied grid maps and node sets.
  • Performed a ROS experiment with a robot and lidar sensor data in a simulated environment.

Main Results:

  • Achieved a 93.98% reduction in search nodes and a 98.94% improvement in time complexity compared to the conventional A* algorithm.
  • Maintained comparable path costs against state-of-the-art algorithms.
  • Demonstrated significant performance improvements in a simulated laboratory environment using ROS and lidar data.

Conclusions:

  • The proposed adaptive cost function and optimal RMB significantly enhance A* algorithm performance for robot path-planning.
  • The method offers a robust and efficient solution for complex navigation tasks.
  • Validated effectiveness through extensive experiments and a real-world simulation.