Energy-Efficient Path Planning for Snake Robots Using a Deep Reinforcement Learning-Enhanced A* Algorithm
Yang Gu, Zelin Wang, Zhong Huang, Yuchuang Tong, Pengyuan Zhao, Heming Jia · 2025
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Description
Yang Gu and co-authors plan, in 2025 work, low-energy paths for snake robots in rough three-way worlds. Such jointed bots thread snow, jungle, sand, and sea floors, and squeeze where wheels fail. Past plans chased the short path and skipped the power bill, which real runs must pay.
A cost model first prices each move's burn. That price then steers a known grid hunt, sharpened by a twin-net reward drill. In fake 3D runs the new hunt burned less than three kin plans: a touch less than the plain hunt, far less than the two-way one. The team backs price-aware plans for field bots.
Catalog record
| CREATOR | Yang Gu, Zelin Wang, Zhong Huang, Yuchuang Tong, Pengyuan Zhao, Heming Jia |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |