Development of a Moving Baseline RTK/Motion Sensor-Integrated Positioning-Based Autonomous Driving Algorithm for a Speed Sprayer

Abstract

To address problems such as pesticide poisoning and accidents during pest control work and to enable efficient work in this area, the development of a competitively prices speed sprayer with autonomous driving is required. Accordingly, in order to contribute to developing the commercialization of a low-cost autonomous driving speed sprayer, we developed a positioning algorithm and an autonomous driving-based spraying algorithm by using two low-cost global navigation satellite system (GNSS) modules and a low-cost motion sensor. In order to provide stable navigation solutions from the autonomous driving hardware despite disturbances from the electromagnetic field generated by the spraying device, the proposed positioning algorithm, a moving baseline (MB) real-time kinematic (RTK)/motion sensor-integrated positioning algorithm, was developed using a loosely coupled extended Kalman filter. To compare the yaw estimation performance provided by the MB RTK positioning technique, yaw was calculated by post-processing with two types of positioning algorithms: the MB RTK/motion sensor-integrated positioning algorithm and the GNSS RTK/motion sensor-integrated positioning algorithm. In the static test, the precision of the yaw provided by the MB RTK/motion sensor-integrated positioning algorithm was 0.14°, but with the GNSS RTK/motion sensor-integrated positioning algorithm, the precision of the yaw was 4.53°. The static test results confirmed that the proposed positioning algorithm using the yaw provided by the MB RTK positioning technique based on two GNSS modules for measurement, precisely estimated the yaw even when the spray engine was operating. To perform autonomous driving and spraying, an autonomous driving-based spraying algorithm was developed using the MB RTK/motion sensor-integrated positioning algorithm. As a result of two performance tests based on the proposed algorithm in an orchard, autonomous driving and spraying were stably performed according to the set autonomous driving route and spraying method, and the root mean square (RMS) of the path-following error was 0.06 m.

Development of Location-Data-Based Orchard Passage Map Generation Method

“…The external dimensions of the autonomous driving speed sprayer were 2.183 m (length) × 1.300 m (width). For detailed specifications of the autonomous driving speed sprayer, please refer to [13]. The positioning hardware for autonomous driving operation was mounted on the top of the middle of the speed sprayer.…”

“…The embedded board was the raspberry pi 4 model, which was used to operate the algorithms related to autonomous driving including positioning, orchard passage map data generation, autonomous driving path generation, and autonomous driving. The positioning algorithm used an MB RTK/Motion sensor integrated positioning algorithm [13], which could provide three-dimensional position, velocity, and attitude.…”

Design of an Automatic Navigation and Operation System for a Crawler-Based Orchard Sprayer Using GNSS Positioning

“…These controllers were applied to the autonomous path tracking of a crop mobile robot. J. H. Han and colleagues proposed an automatic driving spray algorithm for a tracked sprayer, achieving a maximum tracking error of 29 cm [9]. Liang Zhang and his team combined BDS (BeiDou Satellite Navigation System) and IMU (Inertial Measurement Unit) based on kinematic modeling and pure tracking modeling; field tests demonstrated a maximum offset error of less than 10.77 cm [10].…”