Savior ᱡᱚᱛᱚ ᱡᱟᱭ�ga ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ by Mingze Gao is a highly esteemed winner of the very prestigious 2025 A' Design Award ᱠᱟᱥᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ in ᱵᱷᱚᱵᱤᱥᱭᱚᱛ ᱰᱤᱡᱟᱭᱤᱱ Competition Category.
ᱠᱟᱥᱟᱵᱷᱚᱵᱤᱥᱭᱚᱛ· 2025
Savior ᱡᱚᱛᱚ ᱡᱟᱭ�ga ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ
by Mingze Gao
Client: LuXun Academy of Fine Arts
A' Design Award ᱠᱟᱥᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ Winner in ᱵᱷᱚᱵᱤᱥᱭᱚᱛ Category
Verified Award Winner
Verify Award Status for #164686The Design
About Savior ᱡᱚᱛᱚ ᱡᱟᱭ�ga ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ
ᱥᱮᱵᱷᱤᱭᱚᱨ ᱢᱮᱱᱟᱜ-ᱟ ᱢᱤᱫ ᱡᱚᱛᱚ ᱡᱟᱭᱜᱟ ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ ᱡᱟᱦᱟᱸ ᱟᱯᱚᱫᱚ ᱮᱴᱟᱠ ᱡᱟᱭᱜᱟ ᱠᱚᱨᱮ ᱠᱟᱹᱢᱤ ᱞᱟᱹᱜᱤᱫ ᱛᱮᱭᱟᱨ ᱟᱠᱟᱱᱟ ᱾ ᱞᱭᱰᱟᱨ (LiDAR) ᱥᱮᱱᱥᱚᱨ ᱠᱚ ᱚᱛᱮ ᱨᱮ ᱱᱮᱞ ᱨᱮ ᱛᱟᱦᱮᱸᱱᱟ, ᱟᱨ ᱨᱚᱵᱚᱴ ᱮ ᱪᱚᱠᱟ ᱚᱠᱟ ᱡᱟᱭᱜᱟ ᱨᱮ ᱪᱟᱞᱟᱜ ᱞᱟᱹᱜᱤᱫ ᱪᱟᱠᱟ ᱟᱨ ᱛᱮᱥ ᱛᱮᱥ ᱴᱮᱸᱜᱚᱱ ᱠᱚᱨᱮ ᱵᱚᱫᱚᱞ ᱦᱩᱭᱩᱜ-ᱟ ᱾ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱵᱚᱸᱦᱟᱭ ᱛᱮ ᱡᱚᱬᱟᱣ ᱟᱨ ᱨᱮᱥᱠᱭᱩ, ᱰᱟᱠᱛᱟᱨᱤ, ᱛᱨᱟᱸᱥᱯᱚᱨᱴ ᱟᱨ ᱠᱟᱛᱷᱟᱵᱟᱨᱛᱟ ᱢᱚᱰᱭᱩᱞ ᱠᱚ ᱡᱮ ᱡᱟᱦᱟᱸ ᱠᱟᱹᱢᱤ ᱞᱟᱹᱜᱤᱫ ᱟᱡᱟᱜ-ᱟᱡᱟᱜ ᱠᱚ ᱡᱚᱲᱟᱣ ᱠᱚ ᱚᱠᱟᱭ-ᱟ ᱾ ᱦᱟᱭᱰᱨᱚᱡᱮᱱ ᱤᱸᱡᱤᱱ ᱨᱮ ᱮᱴᱟᱜ ᱟᱨ ᱪᱚᱨᱚᱡᱽ ᱮᱭᱟᱭ ᱟᱨ ᱰᱟᱹᱲ ᱚᱠᱛᱚ ᱞᱟᱹᱜᱤᱫ ᱪᱟᱞᱟᱜ ᱮᱟᱢ-ᱟ ᱾ ᱢᱤᱫ ᱠᱩᱨᱩᱢᱩᱴᱩ ᱵᱩᱫᱷᱤ (AI) ᱥᱤᱥᱴᱮᱢ ᱠᱟᱹᱢᱤ ᱨᱮᱱ ᱰᱟᱴᱟ ᱵᱤᱥᱞᱮᱥᱚᱬ ᱠᱟᱛᱮ ᱟᱡᱟᱜ ᱛᱮ ᱮᱭᱟᱭ ᱛᱮᱭᱟᱨ ᱮᱭᱟᱭ ᱾ ᱨᱚᱵᱚᱴ ᱨᱮᱱ ᱢᱟᱯ ᱛᱮᱠᱟᱱᱟ ᱑᱐᱐᱐ ᱡᱚᱛᱚ ᱙᱙᱖ ᱡᱚᱛᱚ ᱔᱒᱘ ᱢᱤᱞᱤᱢᱤᱴᱚᱨ ᱾
The Recognition
About the A' Design Award ᱠᱟᱥᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ
Savior ᱡᱚᱛᱚ ᱡᱟᱭ�ga ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ ᱫᱚ A' Design Award ᱠᱟᱥᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱱᱟᱣᱟ ᱵᱷᱟᱵᱱᱟ ᱛᱮ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Savior ᱡᱚᱛᱚ ᱡᱟᱭ�ga ᱨᱮᱱ ᱢᱚᱰᱭᱩᱞᱟᱨ ᱨᱮᱥᱠᱭᱩ ᱨᱚᱵᱚᱴ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾
Behind the Design
Press Kit
Credits, team members and technical details provided by the designer.
- Unique Properties
- Terrain adaptability can rely on LiDAR sensors. The robot automatically switches wheels and feet according to the terrain and drives in complex terrain. The modular design provides different rescue modules to meet various rescue needs. The hydrogen motor is rechargeable and can be refueled with hydrogen. It has a long endurance and can run for a long time. AI can analyze actions autonomously.
- Dimensions & Packaging
- The dimensions of the robot are 1000mm in length, 996mm in width, and 428mm in height
- Tags
- Intelligent rescue, modular design, hydrogen energy, AI adaptation, disaster relief
- Production Technology
- Manufacturing of core components such as precision robot skeletons and drive systems. AI integration enhances the robot's autonomous analysis capabilities through learning model training. Energy systems are used to develop hydrogen engine charging and endurance technologies.
- Design Challenge
- How to balance the durability and maneuverability of the caster structure. How to improve the endurance of the hydrogen engine. Integrate the accuracy and stability of the AI system to work uninterruptedly in complex environments.
- Research
- This study develops a terrain intelligent rescue robot with modular design, integrated aluminum, and adaptive tire structure, which can quickly perform search and rescue, medical treatment, transportation, communication and other tasks in disaster areas.
Improve rescue efficiency, reduce casualties, and promote the development of emergency rescue technology. - Inspiration
- The inspiration for this design comes from the limitations and inefficiency of traditional rescue methods when natural disasters occur frequently. Faced with complex terrain and harsh environment, traditional rescue equipment often has difficulty reaching the scene quickly, resulting in rescue delays. Therefore, the design team hopes to break through the existing technical bottlenecks through the terrain intelligent rescue robot, improve rescue efficiency, and reduce disaster losses.
- Project Duration
- It starts from March 1, 2024 and ends on June 25, 2024, and is located at Lu Xun Academy of Fine Arts
- Operation Flow
- Start the robot and activate the core functional modules. Select the search and rescue, medical, transportation or communication module according to the mission requirements. The lidar sensor starts to monitor the terrain, and the robot switches the driving mode adaptively. The AI system analyzes the mission data and autonomously plans the action path. After completing the mission, return to the base or wait for further instructions.
- Patents
- Mingze Gao
- Team Members
- Jiangbo Chen
Guangpeng yue
Mingze Gao
Jingquan You
Jiaying Yu
Zhilin Jia
Zhiming Zhang
Zhen Wang
Meishuang Liu
Ze Li
Lanyu Chang
Zihan Gao
Jingqi Wu
Bo Li
Mingxi Li
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