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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

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

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* algorithm continuous jumping constraint mechanism jump point obstacle negotiation wheel-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.