AI-POWERED
🎯
AI-Driven Positioning
人工智能驱动定位
Our machine learning algorithms achieve 20cm positioning accuracy, allowing drones to navigate complex building structures with confidence.
我们的机器学习算法实现了20厘米的定位精度，使无人机能够自信地导航复杂的建筑结构。
90% more accurate than standard drones
比标准无人机精确90%
NEURAL NETWORK
💧
Intelligent Spray Control
智能喷雾控制
Neural networks analyze surface conditions in real-time to optimize spray patterns, water usage, and cleaning effectiveness for each unique surface.
神经网络实时分析表面条件，为每个独特的表面优化喷雾模式、用水量和清洁效果。
75% water reduction vs. traditional methods
比传统方法减少75%用水量
COMPUTER VISION
👁️
Visual Recognition System
视觉识别系统
Computer vision algorithms detect dirt, validate cleaning results, and identify potential obstacles during operation for optimal safety and efficiency.
计算机视觉算法检测污垢、验证清洁结果，并在操作过程中识别潜在障碍物，以实现最佳安全性和效率。
95% dirt detection accuracy
95%的污垢检测准确率
AUTONOMOUS
🚗
Automated Support Vehicle
自动支持车辆
Our robotic ground system provides automatic landing, water refilling, and battery recharging, enabling continuous operation without human assistance.
我们的机器人地面系统提供自动着陆、加水和电池充电，实现无人工协助的连续运行。
85% reduction in operation downtime
减少85%的操作停机时间
SMART ENERGY
⚡
Extended Flight Time
延长飞行时间
Our support vehicle enables continuous operations through rapid battery swapping and automatic water refilling systems, eliminating downtime.
我们的支持车辆通过快速电池更换和自动加水系统实现连续操作，消除停机时间。
3x longer operational time
运行时间延长3倍
DEEP LEARNING
🧠
Adaptive Cleaning Algorithms
自适应清洁算法
Our drones learn from each cleaning operation, continuously optimizing their approach based on building architecture, dirt patterns, and environmental conditions.
我们的无人机从每次清洁操作中学习，根据建筑结构、污垢模式和环境条件不断优化其方法。
65% better cleaning with each cycle
每个周期清洁效果提高65%
OPEN SOURCE
🔓
Open Source Positioning Code
开源定位代码
We contribute to the robotics community by sharing our core positioning algorithms as open source, enabling developers to build upon our research and accelerate innovation.
我们通过将核心定位算法开源来为机器人社区做贡献，使开发人员能够在我们的研究基础上构建并加速创新。
BIG DATA
📊
Accumulated Testing Data
累积测试数据
Our AI systems continuously improve through analysis of over 10,000 hours of real-world testing data, covering diverse building types, weather conditions, and cleaning challenges.
我们的AI系统通过分析超过10,000小时的实际测试数据不断改进，涵盖各种建筑类型、天气条件和清洁挑战。
98% system improvement
系统改进98%