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

Currently, pest control work using speed sprayers results in increasing numbers of safety accidents such as worker pesticide poisoning and rollover of vehicles during work. To address this, there is growing interest in autonomous driving technology for speed sprayers. To commercialize and rapidly expand the use of self-driving speed sprayers, an economically efficient self-driving speed sprayer using a minimum number of sensors is essential. This study developed an orchard passage map using location data acquired from positioning sensors to generate autonomous driving paths, without installing additional sensors. The method for creating the orchard passage map presented in this study was to create paths using location data obtained by manually driving the speed sprayer and merging them. In addition, to apply the orchard passage map when operating autonomously, a method is introduced for generating an autonomous driving path for the work start point movement path, work path, and return point movement path.

Efficacy and Safety of Paliperidone Palmitate 6-Month versus Paliperidone Palmitate 3-Month Long-Acting Injectable in European Patients with Schizophrenia: A Post Hoc Analysis of a Global Phase-3 Double-Blind Randomized Non-Inferiority Study.

Purpose: To examine efficacy and safety of paliperidone palmitate (PP) 6-month (PP6M) vs PP3-month (PP3M) long acting injectable (LAI) in patients with schizophrenia from European sites previously stabilized on PP3M or PP1-month (PP1M). Methods: This post-hoc subgroup analysis used data from a global phase-3 double-blind (DB) randomized non-inferiority study (NCT03345342). Patients were randomized (2:1, respectively) to receive dorsogluteal injections of PP6M (700 mg eq. or 1000 mg eq.) or PP3M (350 mg eq. or 525 mg eq.) in the 12-month DB phase. Primary endpoint was time-to-relapse during the DB phase, using a Kaplan-Meier cumulative survival estimate (non-inferiority margin 95% CI lower bound larger than prespecified as -10%). Treatment emergent adverse events (TEAEs), physical examinations, and laboratory tests were also evaluated. Results: A total of 384 patients who entered the DB phase were included in European sites (PP6M, n = 260; PP3M, n = 124) with a mean age similar in both groups (mean age [SD] years: PP6M, 40.0 [11.39]; PP3M, 38.8 [10.41]). Baseline characteristics were similar across both groups. The number of patients who experienced a relapse during DB phase were PP6M: 18 (6.9%) vs PP3M: 3 (2.4%) with percentage relapse-free difference of -4.9% (95% CI: -9.2%, -0.5%), thus achieving non-inferiority criteria. Secondary efficacy endpoints indicated comparable improvements. Incidence of TEAEs was similar between PP6M (58.8%) and PP3M (54.8%) groups. Nasopharyngitis, headache, increased weight, and injection-site pain were the most common TEAEs. Conclusion: The efficacy of PP6M was non-inferior to that of PP3M in preventing relapse in the European subgroup previously treated with PP1M or PP3M, which was consistent with the global study. No new safety signals were identified.

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

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.

Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment.

To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively.

Medium to Long Range Kinematic GPS Positioning with Position-Velocity-Acceleration Model Using Multiple Reference Stations.

In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple reference stations to improve the reliability of the solutions. In this study, GPS kinematic positioning algorithms are developed for the medium to large-scale network based on the position-velocity-acceleration model. Hence, the algorithm can perform even in cases where the near-constant velocity assumption does not hold. In addition, the estimated kinematic accelerations can be used for the airborne gravimetry. The proposed algorithms are implemented using Kalman filter and are applied to the in situ airborne GPS data. The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values. The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.