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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Self-adaptive search algorithm for path planning based on the A* algorithm.

Shiwei Lin1, Xiangxi Fan2, Zhixuan Xie2

  • 1School of Computer Engineering, Jimei University, Xiamen, 361000, Fujian, China. Shiwei.Lin@jmu.edu.cn.

Scientific Reports
|December 29, 2025
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Summary
This summary is machine-generated.

The Obstacle Density-based Dynamic Exponential A* (ODDEA*) algorithm improves robot path planning by adjusting heuristic weights based on obstacle density. This novel approach significantly reduces planning time and search space compared to traditional A* methods.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Science

Background:

  • The A* algorithm is crucial for robot global path planning.
  • Existing A* methods struggle with redundant nodes and large search spaces.

Purpose of the Study:

  • To introduce the Obstacle Density-based Dynamic Exponential A* (ODDEA*) algorithm.
  • To enhance robot path planning efficiency by addressing A* limitations.

Main Methods:

  • The ODDEA* algorithm dynamically adjusts heuristic function weights based on surrounding obstacle density.
  • A local dynamic penalty is incorporated to guide robots toward low-obstacle-density areas.
  • Computational experiments compare ODDEA* against Theta*, A*, and BA* on various grid map sizes.

Main Results:

  • ODDEA* significantly reduces expanded nodes and planning time compared to Theta*, A*, and BA*.
  • On fixed grid maps, ODDEA* achieved 46.96% of the planning time and 20.33% of the search space compared to A*.
  • Experiments included small (20x20), medium (40x40), large (60x60) grid maps, and 50 random medium maps.

Conclusions:

  • The ODDEA* algorithm offers superior performance in robot path planning.
  • ODDEA* effectively minimizes computational resources by optimizing search space and reducing planning time.
  • This algorithm presents a promising advancement for efficient robotic navigation in complex environments.