Precision Agriculture Route Optimization with Crop Row Constraints
Disciplines
Robotics | Theory and Algorithms
Abstract (300 words maximum)
Route Optimization is an ongoing topic of research for Unmanned Ground Vehicles (UGV) where minimizing task completion time and energy usage are typically considered. In our research, we propose a route optimization method tailored for Precision Agriculture applications in which tasks are located inside planted fields. First, aerial images are taken of the farmland to be processed by a base station. Next, the boundaries of the crop fields, orientation of the crop rows, and UGV task points are extracted from the image. Using these image parameters, a map is created to solve a Traveling Salesman Problem (TSP) with the following constraints: minimize UGV energy usage, minimize UGV travel distance, and travel must always be parallel to crop row orientation within the boundary of a planted field. Finally, the UGV executes the generated route to accomplish all tasks. To evaluate the performance of different TSP approximation algorithms with the given crop row constraints a simulated environment is used. Ultimately, our framework aims to optimize UGV performance for Precision Agriculture tasks and effectively utilize aerial imaging in air to ground agricultural applications.
Use of AI Disclaimer
no
Academic department under which the project should be listed
SPCEET – Robotics and Mechatronics Engineering
Primary Investigator (PI) Name
Muhammad Hassan Tanveer
Precision Agriculture Route Optimization with Crop Row Constraints
Route Optimization is an ongoing topic of research for Unmanned Ground Vehicles (UGV) where minimizing task completion time and energy usage are typically considered. In our research, we propose a route optimization method tailored for Precision Agriculture applications in which tasks are located inside planted fields. First, aerial images are taken of the farmland to be processed by a base station. Next, the boundaries of the crop fields, orientation of the crop rows, and UGV task points are extracted from the image. Using these image parameters, a map is created to solve a Traveling Salesman Problem (TSP) with the following constraints: minimize UGV energy usage, minimize UGV travel distance, and travel must always be parallel to crop row orientation within the boundary of a planted field. Finally, the UGV executes the generated route to accomplish all tasks. To evaluate the performance of different TSP approximation algorithms with the given crop row constraints a simulated environment is used. Ultimately, our framework aims to optimize UGV performance for Precision Agriculture tasks and effectively utilize aerial imaging in air to ground agricultural applications.