Inspiration
- Addressing the need for enhanced healthcare logistics, especially during crises.
- Aimed at improving response times and patient care in busy hospital environments.
What it does
- Autonomous robot for swift and safe delivery of medical supplies.
- Utilizes advanced sensors, mapping technology, and machine learning for efficient navigation.
- Reduces human workload and ensures timely deliveries.
How we built it
- Combination of cutting-edge robotics technology and AI algorithms for detection (OpenCV YOLO model)
- Robust chassis with precision sensors for mapping and obstacle avoidance.
- Machine learning for route optimization and adaptation to dynamic layouts.
- User-friendly interface
Challenges we ran into
- Precise navigation
- Being able to connect the phone camera to the model on the PC.
- Using the ESP32 microcontroller (getting it to actually implement our path algorithm) as we hadn't used it before
Accomplishments that we're proud of
- Demonstrated ability to detect key objects with speed & accuracy.
What we learned
- Insights into robotics, AI, and healthcare logistics.
What's next for Medical Assistance Delivery Robot
- Enhancing AI algorithms for precise navigation.
- Expanding compatibility with a broader range of medical supplies.
- Integration with hospital management systems for seamless communication.
- Real-world deployment for impactful improvements in medical supply logistics.
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