Project Overview
Project TreeStrike is an innovative, autonomous drone-based reforestation system designed to enhance environmental restoration efforts. The system utilizes advanced drone technology, onboard AI, and high-resolution imaging to identify optimal planting sites and efficiently deploy seed bombs.
How It Works
- High-Altitude Imaging: The primary drone captures high-altitude images or LiDAR data to locate the base station and assess the terrain.
- Onboard Analysis: AI algorithms process the collected data to determine the best planting locations based on terrain suitability.
- Optimized Flight Planning: The system calculates optimal flight paths that adapt to wind conditions, scheduling flights appropriately (e.g., morning outbound and evening return or nighttime operations).
- Autonomous Docking & Charging: Upon return, the drone autonomously docks at the base station to recharge via photovoltaic panels and receive seed bomb resupply.
- Seed Deployment: A secondary drone, specialized for deploying seed bombs, follows the calculated trajectory to plant seeds in the designated areas.
Technical Enhancements
- Autonomous Charging Mechanism: Efficient recharging through an automated docking process.
- Real-Time Weather Integration: Incorporation of local weather data to adjust flight paths and optimize operational timing.
- 3D Imaging & RTK GPS Integration: High-precision mapping with up to 1 cm positional accuracy to construct detailed 3D terrain models.
- Offline Operation: Pre-downloaded environmental resources ensure reliable functionality even with limited internet connectivity.
Team and Collaboration
Project TreeStrike is a collaborative initiative supported by a multidisciplinary team including AI & Machine Learning Engineers, Autonomous Navigation Specialists, Embedded Systems Developers, and Geospatial Analysts. Regular meetings and coordinated efforts ensure smooth progress, while industry partners contribute expert guidance and resources.
Prerequisites and Workload
- Prerequisites: Participants with a background in AI, drone technology, or programming are preferred. However, those without prior experience can bridge the knowledge gap with dedication and effort.
- Expected Workload: The estimated workload is 5-7 hours per week, depending on prior knowledge. This includes research, coding, discussions, and project-related tasks.
Industry Support and Call for Participation
Our project benefits from industry support in RTK GPS technology and sustainable agriculture practices. We welcome passionate individuals to join our team. For more information or to participate, please contact jasper.golembiewski@stud.hochschule-heidelberg.de.