Pancreas robot - Dahms, Calvin (Kansas State University, 2022)
The World continues to need increased production of food to sustain the expected population growth over the next 30 years. The global population is projected to be more than 9.7 billion people by 2050. These people will need to eat nearly double the amount of food that is produced today. There are many approaches to solving this food crisis dilemma and progress is being made on multiple fronts. Sources of production growth include larger and more precise machine technology; more robust fertilizer applications; and crop modeling for genetically enhanced phenotyping. This paper focuses on the use of robotic technology to help model crop growth by using an unmanned ground vehicle (UGV) for data collection. Simple crop modeling helped the agricultural revolution in the 20th century. Today, scientists are building upon those models to create more complex ways to represent crops and their traits. These models require large amounts of data to observe and describe relationships between inputs and crop responses. This data needs to be dependable, consistent, and as close to the source as possible. To achieve that type of data for this project, a UGV was developed to traverse rugged field conditions. The UGV was designed to carry a Geophex Electromagnetic (EM) sensor that measures the electrical conductivity of the soil. This electrical conductivity will be used to decipher soil characteristics that underlie the growth potential of different wheat traits. The robot that carries the EM sensor must be designed to not interfere with the conductivity measurements of the sensor. The data collected must be accurate and repeatable. The scope of this research project is to develop the UGV to carry the sensor through the harsh field environments while not interfering with the incoming EM signal from the sensor. The project also explores methods for the robot to navigate through its environment on its own to limit human influence on the recorded data.
Keywords: Robotics; Agriculture; Autonomous; Sensing