University of East Anglia
Agricultural robots are widely used to tackle labour-intensive, repetitive, and physically demanding tasks. Precise localization and mapping are important to guide the robots to complete the tasks in the field. Global Navigation Satellite System (GNSS) based systems are widely used in agricultural applications for localization purposes. However, these GNSS standalone-based localization systems can only achieve accuracy in the range of 1-10 meters, so the robots usually follow the long corridors in regular crop fields. The walls (e.g., plants) are detected by LiDAR, which is used to calibrate the robot’s location. Moreover, in many agricultural environments, satellite signals are unavailable or inaccurate, or long corridors and regular field scenarios are not available, which leads to the need for advanced solutions independent from these signals.
The proposed solution can achieve accuracy in the range of 5-10 cm and has the following work packages (WPs).
WP1: Use GPS / RTK for outdoor localization of the robot.
WP2: Use App (camera/drone) to plot the field map and upload it to the Cloud server for processing. Based on the data sent from App, the Cloud server gets back route info to the robot so that the robot can automatically follow the route info in the field. The route info is based on the complete coverage path planning algorithm to cover all the field areas with the shortest path.
WP3: Use cameras and LiDAR to detect and analyse objectives so that the robot can do agri-tasks.
WP4: Raspberry Pi 4 + Jetson Xavier NX powered vehicle, which can move based on time series location data from WP1 and processing camera and LiDAR data from WP3. Communication modules (e.g., 4G/5G, Wi-Fi) are used to connect a smart device and the Cloud server.
Primary supervisor: Edwin Ren ([email protected])
Start date: October 2023
For more information on this project, please visit www.uea.ac.uk
Entry requirements: Acceptable first degree in Computer Science, Telecommunications, Electrical Engineering or other numerate discipline.
This PhD project is in a competition for studentships allocated to the School of Computing Sciences as a direct result is increased PGT student fee income for the MSc Courses in Cyber Security, Data Science and Computing Sciences. All successful candidates will be expected to support PGT Lab sessions from October 2023 and related activities as allocated in support of these programmes within the working hours permitted for full-time Postgraduate Researchers.
Funding comprises ‘home’ tuition fees and an annual stipend (2022/23 rate is £17,668, 2023/24 tbc) for a maximum of 3 years.
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