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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

Energy-Efficient Path Planning for Snake Robots Using a Deep Reinforcement Learning-Enhanced A* Algorithm.

Yang Gu^1,2, Zelin Wang^1,2, Zhong Huang^1,2

¹School of Information and Communication Engineering, Hainan University, Haikou 570228, China.

Biomimetics (Basel, Switzerland)

|December 24, 2025

Summary

This summary is machine-generated.

This study introduces an energy-efficient path planning method for snake-like robots using an improved A* algorithm with deep reinforcement learning. The approach significantly reduces energy consumption in complex 3D environments.

Keywords:

A* algorithm deep reinforcement learning energy consumption path planning snake robot three-dimensional space

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Area of Science:

Robotics
Artificial Intelligence
Path Planning

Background:

Snake-like robots offer superior mobility in challenging terrains due to their flexibility.
Current path planning methods often prioritize shortest paths over energy efficiency.
Efficient navigation is crucial for snake robot operations in confined or rugged environments.

Purpose of the Study:

To develop an energy-efficient path planning method for snake-like robots.
To enhance the safety, energy efficiency, and task performance of snake robots.
To address the limitations of existing path planning algorithms that neglect energy optimization.

Main Methods:

Developed an Energy Consumption Estimation Model (ECEM) for 3D motion.
Integrated ECEM into a heuristic function for an improved A* algorithm.
Enhanced the A* algorithm with Deep Reinforcement Learning (Dueling Double-Deep Q-Network - D3QN).

Main Results:

The proposed D3QN-enhanced A* algorithm significantly reduced energy consumption.
Achieved 3.39% to 27.26% lower energy consumption compared to traditional A* and bidirectional A* algorithms.
Demonstrated effective path planning in complex 3D environments.

Conclusions:

The integration of deep reinforcement learning and adaptive heuristics improves snake robot path planning.
The proposed method enhances energy efficiency and practical applicability for snake robots.
This approach offers a viable solution for energy-conscious navigation in complex terrains.