Deep Learning-based Heterogeneous System for Autonomous Navigation

Sellers, T., Lei, T., Carruth, D. W., & Luo, C. (2023). Deep Learning-based Heterogeneous System for Autonomous Navigation. Proc SPIE 12539, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VIII. Orlando, FL, USA: SPIE. 12539. DOI:10.1117/12.2665844.

Abstract

In order to face the everyday growing population in today's world, the deployment of autonomous vehicles is a promising direction for precision agriculture. Autonomous vehicles (AVs) have been developed and deployed for various agricultural needs such as field planting, harvesting, soil collection, and crop data collection. One method of achieving those task is complete coverage path planning (CCPP), which constructs a continuous path that covers a wide area of interest. However, in a large farm with multiple fields, those tasks have been extremely complicated and computationally expensive on a navigation system when utilizing a single AV. A heterogeneous system is proposed to sense the fields and solve the navigation and routing problem within multi-field path planning. We developed a deep learning-based routing scheme for Unmanned Aerial Vehicles (UAVs) to sense mature crops for harvest. The deep learning routing scheme utilizes a goal embedding feature and coordinate position feature to generate an optimal path for the Unmanned Aerial Vehicles, which allows them to find several candidate solutions. A deep learning-based complete coverage path planning (DL-CCPP) navigation scheme is also proposed for our Unmanned Ground Vehicle (UGVs) to navigate through the fields and collect the mature crops within them. The DL-CCPP uses UAV's images in its deep learning network to construct the CCPP path from the AV coordinates.