Project
- Development of AR/XR Enhanced Systems: Create AR/XR environments that provide real-time information to farmers during critical tasks such as pruning and thinning, ensuring precision without disturbing surrounding trees.
- Collaborative Multi-Agent Integration: Design systems that seamlessly integrate robots (for precise operations), IoT devices (for environmental monitoring), AR/XR interfaces (for immersive information and interaction), and human workers (for collaborative decision-making).
- Data Visualization and Utilization: Utilize AR/XR to visualize and analyze data from IoT sensors, enabling farmers to make informed decisions based on comprehensive insights into orchard health and productivity.
- Scalability and Adaptability: Ensure the developed systems can be adapted for various crops, expanding their applicability beyond apple orchards.
- Cost and Compatibility: Investigate cost-effective solutions that are compatible with existing orchard infrastructure, including potential partnerships with hardware manufacturers.
- Communication Efficiency: Optimize data flow between AR/XR systems and robots to ensure smooth, real-time interaction without delays.
- Human-Machine Interaction: Design intuitive interfaces for farmers using robots and AR/XR tools to minimize distractions and enhance productivity.
- Prototyping and testing AR/XR systems for agricultural guidance.
- Enhancing robot autonomy to work effectively alongside AR/XR systems.
- Developing methods to incorporate IoT data into AR/XR environments for enhanced decision-making.
- Conducting experiments in real apple orchards to validate system efficiency and safety.
- A comprehensive framework for integrating AR/XR technologies with collaborative multi-agent systems in agriculture.
- Enhanced methodologies for pruning, thinning, and harvesting that improve productivity while reducing environmental impact.
- Insights into the practical implementation of these technologies, including areas for further innovation and scalability.
Profile
- Python: Proficiency in machine learning frameworks like TensorFlow or PyTorch.
- C++: Basic understanding for system-level programming.
- JavaScript: Familiarity with web-based interfaces.
- Augmented reality: Experience with relevant frameworks
- Robotics control software: Experience with ROS2, relevant packages and algorithms for robotic systems programming.
- Sensors and IoT: Basic understanding to interface with environmental sensors.
Offer
The selected PhD students will be offered a 1-year contract, once renewable with 3 more years after positive evaluation. The salary will be commensurate to the standard scale for PhD students in Belgium; it includes social and medical insurance as well as pension rights. Funding for the full PhD (4 years) is guaranteed, but the selected candidate will be encouraged to later submit a proposal for an FWO PhD Fellowship application (https://www.fwo.be/en/fellowships-funding/phd-fellowships/phd-fellowship-fundamental-research) as a possibility to acquire additional funding and gain valuable grant-application writing skills.
The successful PhD applicants will have to register at, and comply with, the regulations of the KU Leuven’s Arenberg Doctoral School before final acceptance. Good knowledge of the English language is a requirement to be approved by the doctoral school. The successful PhD applicant will follow a doctoral programme including personal training in management, science communication, and teaching. As part of the doctoral requirements, the student will have to take up a teaching task of on average 2 hours per week.
Interested?
The application will require:
– A curriculum vitae;
– A list of all master courses with corresponding ECTS and individual scores obtained;
– A statement of research interests or research proposal (10pt font, max 3 pages plus references);
– Up to 3 letters of reference to be directly sent by the reference person(s) to: nikolaos.tsiogkas@kuleuven.be
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.
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