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Related Experiment Video

Updated: Jan 16, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Obstacle Crossing Path Planning for a Wheel-Legged Robot Using an Improved A* Algorithm.

Jinliang Lu1, Ming Pi1, Guoxin Zeng1

  • 1School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances A* path planning for wheel-legged robots, improving obstacle avoidance in complex environments. The new algorithm significantly reduces path nodes and planning time for efficient autonomous navigation.

Keywords:
A* algorithmcontinuous jumping constraint mechanismjump pointobstacle negotiationwheel-legged robot

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Path Planning Algorithms

Background:

  • Wheel-legged robots face challenges in obstacle avoidance and terrain negotiation in complex static environments.
  • Efficient path planning is crucial for autonomous navigation and operational efficiency.

Purpose of the Study:

  • To introduce an enhanced A* path planning algorithm for wheel-legged robots.
  • To improve obstacle traversal efficiency and motion stability in complex environments.

Main Methods:

  • Incorporated jump-point search, dynamically weighted heuristics, and continuous jumping constraints into the A* algorithm.
  • Extended the 8-neighborhood rule to support diagonal jumps.
  • Implemented redundant point removal and Bézier curve smoothing for path optimization.

Main Results:

  • Achieved an 85% reduction in search nodes (from 542 to 78) compared to standard A*.
  • Reduced planning time to 0.0032 seconds.
  • Demonstrated enhanced performance in crossing complex structures and improved motion stability.

Conclusions:

  • The enhanced A* algorithm significantly improves path planning efficiency for wheel-legged robots in complex static environments.
  • The algorithm enhances autonomous navigation capabilities and motion stability.
  • This research contributes to more efficient real-time path planning for robotic systems.