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To address the poor performance of the existing sweet potato transplanting robots, in this paper, a two-degree-of-freedom sweet potato transplanting robot arm is proposed to improve the transplanting success rate and production yield. Different transplanting strategies, including vertical transplanting, boat-shaped transplanting, and inclined planting, can be achieved for different terrain types. The designed transplanting mechanical arm can achieve both vertical and rotational movement of the seedling claw through the vertical sliding motor and the rotating motor respectively. With the two degrees of freedom, the designed transplanting robot arm can complete various transplanting trajectories on the transplanting plane. Moreover, a transplanting trajectory controller is then designed for the proposed transplanting robot based on a linear model prediction algorithm. To improve the control performance, two control system optimization schemes are proposed: the influence of prediction time length on the accuracy of transplanting trajectory is discussed and the optimal prediction time length value is obtained, and a reference trajectory segmenting control was proposed to improve the real time of sweet potato transplanting. The proposed sweet potato transplanting robot is validated experimentally through field testing. The minimum qualified rate of potato seedling erection angle was 94.7%, and the minimum qualified rate of planting depth was 94.8%. The test proved that the proposed transplanting scheme can realize various transplanting methods of sweet potato with better performance, which satisfies the practical production requirements for mechanical planting of sweet potatoes.

BackgroundIdiopathic short stature (ISS) describes a heterogeneous group of children of many unidentified causes of short stature presently without definitive therapy. Chinese herbal medicine (CHM) is an alternative and complementary treatment for children with ISS and has been widely used for ISS while the evidence of its effectiveness is controversial. We conducted this systematic review and meta-analysis in order to evaluate the efficacy of CHM for ISS.MethodsPubMed, Embase, Web of science, Sino-Med, Cochrane, CNKI, VIP, and Wangfang Data were electronically searched to collect randomized controlled trials (RCTs) of CHM treatment of ISS from inception to May 2021. Two researchers independently scanned the literature and extracted information on general characteristics, including patient, study design, interventions, and side effects, assessing the CHM intervention's efficacy and the risk of bias. Height, bone age, growth velocity, and IGF-1 level are the main consequences. Height standard deviations score (HtSDS), change in HtSDS (ΔHtSDS), osteocalcin, the peak level of growth hormones (GHP), and predicted adult height (PAH) are the secondary outcomes. Meta-analysis was then performed by using RevMan 5.3 (Cochrane Collaboration).ResultsSeven articles (569 participants) were included. The Meta-analysis indicated that herbal medicine was associated with increased height (MD 2.16 points; 95%CI, 0.22 to 4.10; P = 0.03), growth velocity (MD 1.47 points; 95%CI, 0.28 to 2.67; P = 0.02), IGF-1 level (MD 28.13 points; 95%CI, 22.80 to 33.46; PConclusionAccording to current research, CHM appears to be useful for children with ISS. Due to the limited quality and number of studies included, more high-quality studies are needed to corroborate the above conclusions.

Traditional sweet potato transplanters have the problem of seedling leakage and can only accomplish one transplantation method at a time, which does not meet the requirements of complex planting terrain that requires multiple transplantation methods. Therefore, this paper proposes a design for a crawler-type sweet potato transplanting machine, which can accomplish a variety of transplanting trajectories and conduct automatic replanting. The machine has a transplanting piece and a replanting piece. The transplanting piece completes the transplanting action through a transplanting robot arm, and the replanting piece detects the transplanting status by deep learning. The mathematical model of the transplanting robot arm is built, and the transplanting trajectory is inferred from the inverse kinematics model of the transplanting robot. In the replanting piece, a target detection network is used to detect the transplanting status. The DBIFPN structure and the CBAM_Dense attention mechanism are proposed to improve the accuracy of the target detection of sweet potato seedlings. The experiment showed that the transplanting robot arm can transplant sweet potatoes in horizontal and vertical methods, and the highest transplanting qualification rate is 96.8%. Compared with the use of the transplanting piece alone, the leakage rate of the transplanting–replanting mechanism decreased by 5.2%. These results provide a theoretical basis and technical support for the research and development of sweet potato transplanters.

The mechanization of ship-shaped transplanting is currently an urgent problem that should be solved. The movement trajectory of the transplanting mechanism is the key technology to perform ship-shaped transplanting. In this study, a shipshaped transplanting trajectory was built and a mathematical model of a four-link transplanting mechanism was developed based on the requirements of the sweet potato transplanting agronomic technology. The particle swarm optimization algorithm was used, with the length of the four-bar mechanism and the installation angle of the fixed bar serving as the variables to optimize. The objective was to minimize the deviation of the ship-shaped transplanting trajectory, yielding an iterative optimization solution. The MATLAB simulation results showed that the penalty factors of different proportions in the adaptive particle swarm optimization algorithm affected the transplanting trajectory. The optimal penalty factor parameters are a=0.6, ß=0.4. They ensure that the transplanting trajectory fulfills the agronomic requirements, and limit the deviation in the return trajectory of the mechanism. The sizes of the optimized four-bar mechanism were 110, 312, 245, 160, 360, and, 160 mm. The determined installation angle was 100°. The results of the field experiments demonstrated that the optimized four-bar transplanting mechanism can better fulfill the agronomic technical requirements of sweet potato ship-shaped transplanting. For a transplanting speed of 0.2 m/s, the average qualified rates of insertion depth, insertion length, and tail height were equal to 94.00%, 93.83%, and 91.67%. The results obtained in this study provide a theoretical basis and technical support for studying and developing sweet potato ship-shaped transplanting machinery. [ABSTRACT FROM AUTHOR]

To improve the trajectory tracking robust stability of agricultural vehicles, a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented. Through the proposed method, the path tracking problem can be divided into two problems with speed and steering angle constraints: the trajectory planning problem, and the trajectory tracking optimization problem. Firstly, the nonlinear kinematics model of the agricultural vehicle was discretized, then the derived model was inferred and regarded as the prediction function plant for the designed controller. Second, the objective function characterizing the tracking performance was put forward based on system variables and control inputs. Therefore, the objective function optimization problem, based on the proposed prediction equation plant, can be regarded as the nonlinear constrained optimization problem. What's more, to enhance the robust stability of the system, a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control. To validate the theoretical analysis before, the Matlab simulation was performed to investigate the path tracking performance. The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability. Meanwhile, the corresponding experiments were conducted. When the test vehicle tracked the reference track with a speed of 3 m/s, the maximum lateral deviation was 13.36 cm, and the maximum longitudinal deviation was 34.61 cm. When the added horizontal deviation disturbance Yr was less than 1.5 m, the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive. Finally, to better highlight the controller proposed in this paper, a comparison experiment with a linear model predictive controller was performed. Compared to the conventional linear model predictive controller, the horizontal off-track distance reduced by 36.8% and the longitudinal deviation reduced by 32.98% when performing circular path tracking at a speed of 3 m/s. [ABSTRACT FROM AUTHOR]

The obstacle avoidance controller is a key autonomous component which involves the control of tractor system dynamics, such as the yaw lateral dynamics, the longitudinal dynamics, and nonlinear constraints including the speed and steering angles limits during the path-tracking process. To achieve the obstacle avoidance ability of control accuracy, an independent path re-planning controller is proposed based on ROS (Robot Operating System) nonlinear model prediction in this paper. In the design process, the obstacle avoidance function and an objective function are introduced. Based on these functions, the obstacle avoidance maneuvering performance is transformed into a nonlinear quadratic optimization problem with vehicle dynamic constraints. Moreover, the tractor dynamics maneuvering performance can be effectively adjusted through the proposed objective function. To validate the proposed algorithm, a ROS based tractor dynamics model and the SLAM (Simultaneous Localization and Mapping) are established for numerical simulations under different speed. The maximum obstacle avoidance deviation in the simulation is 0.242 m at 10 m/s, and 0.416 m at 30 m/s. The front-wheel rotation angle and lateral velocity are within the constraint range during the whole tracking process. The numerical results show that the designed controller can achieve the tractor obstacle avoidance ability with good accuracy under different conditions. [ABSTRACT FROM AUTHOR]