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Introduction: Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. Methods: This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. Results and discussion: YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. Methods: This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. Results and discussion: YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics. [ABSTRACT FROM AUTHOR]

While dealing with dynamic scenarios, mobile robot (MR) path planning (PP) has proven to be extremely challenging. To overcome difficulties with grid-map representation, the paper proposes a hybrid path planning strategy. In a typical setting, an A-star algorithm first generates a global path planning (GPP). To ensure safe obstructive avoidance, the modified heuristic function is enhanced with a risk cost coefficient adjusted according to the distance to the nearest obstacle. Then, GPP is improved by eliminating redundant nodes, and smoothing algorithms. Furthermore, local path planning (LPP) has enhanced the ability to avoid obstacles in local areas by using adaptive dynamic window approach (DWA). The obtained path is hardly continuous after eliminating unnecessary path nodes and the required recalculation LPP based on DWA. Consequently, straight, and curved segments are joined into the continuous polyline smoothed by the Bezier curves. The proposed hybrid PP has been shown to be feasible and to produce optimal outcomes in both simulations and experiments. [ABSTRACT FROM AUTHOR]

Evaluating building damage in mining areas using Interferometric Synthetic Aperture Radar (InSAR) has inherent challenges. This work presents a Delaunay Triangulation of Surface Deformation (DTSD) model that utilizes Permanent Scatter InSAR to analyze various types of deformations in villages. This model created a network of irregular triangles using Delaunay triangulation and PS points as the input. The calculation involved determining the inclination of triangular edges and the torsion of spatial Bezier curves fitted by these edges. The PS points' maximum inclination and curvature values were identified by solving over-determined equations. Results from a slurry injection mine in Huaibei, China, indicate that the various types of deformations in the neighboring communities were below 8 %, and the buildings experienced minimal damage. The DTSD model offers a theoretical framework for monitoring and assessing various types of surface deformation and designing essential zones for building protection. [ABSTRACT FROM AUTHOR]

This article presents a generalized model of two-axis flexure hinges based on quadratic rational Bézier curve. The generalized closed-form compliance equations are derived based on the virtual work theory and the superposition relationship of the deformation. Then, how to determine the number of sensitive axes, the location of the primary and secondary sensitive axes, and the configuration of the notch profile are discussed. There are 20 types of notch profiles derived from single or mixed, symmetrical or asymmetrical curves. And, the correctness of the compliance equations is verified by finite-element analysis. The maximum relative error does not exceed 10%. Finally, the precision of rotation, the maximum stress, and the effect of structural parameters on the compliance are analyzed. The results show that for two-axis flexure hinges with a flush single curve notch profile, the proximity of the center of rotation to the load end does not significantly affect the axial compliance as well as the torsional compliance. For hybrid two-axis flexure hinges with symmetric structure, the ability to maintain the center of rotation under lateral forces is better than that under the same torque. The proposed generalized model provides a reference for the design of spatial compliant mechanisms. [ABSTRACT FROM AUTHOR]

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development. [ABSTRACT FROM AUTHOR]

Objective Route planning is the foundation of the safe and efficient navigation of ships. Due to insufficient consideration of the requirements for autonomous navigation unmanned ships, the current route planning method has two problems: (1) Own to the insufficient consideration for the motion constraints of ships in the planning route, the unmanned ship tends to produce the track deviation near the turning position and is likely to cross the navigation obstruction area while sailing autonomously. (2) The planned route is difficult to guide the unmanned ship to sail autonomously through the specified place flexibly with the course and speed for a specific task. Therefore, we propose a route planning method for autonomous navigation of the unmanned ship with a maximum navigable window sequence that constrains Bezier curves.Methods First, the navigation obstruction areas are calculated on the basis of electronic navigation charts, the maximum navigable window sequence that does not intersect with the navigation obstruction area is extracted along the pre-generated route, and the navigable constraint space is constructed. Then, the potential of the Bezier curve for the calculation of related motion parameters in the constraint envelope area of control points is excavated, and the route planning is transformed into a problem for the control point of Bezier curves through convex optimization under the double constraints of space and motion parameters. Finally, the method is compared with the existing methods through simulation and real ship experiments.Results The experimental results show that: (1) This method could effectively reduce the oscillation of track tracing of the unmanned ship at the turning position and significantly improve the tracking control accuracy of the unmanned ship. The maximum horizontal and vertical errors and variances of the track tracing of the route generated by the method are significantly less than those of the improved binary tree method of the route. (2) The actual speed and course are basically consistent with the set speed and direction, which shows that this method could guide the unmanned ship to sail autonomously through the specified place at the speed for a specific task.Conclusions (1) The method could restrain the ship's motion to plan the route, so as to improve the accuracy of the unmanned ship effectively in executing the planned route. (2) The method could guide the unmanned ship to navigate autonomously through the specified place with the motion parameters such as heading and speed required by the specific task, so as to achieve the purpose of flexible mobility. [ABSTRACT FROM AUTHOR]

Efficient and safe path planning for autonomous navigation is paramount in advancing the motion control capabilities of mobile robots. To obtain the global optimal smooth path for mobile robots, a multi-strategy ensemble Harris hawks optimization algorithm (SDHHO) is proposed in this paper. The spiral search strategy is adopted to improve the early update method of the algorithm, which can improve the global exploration ability. To achieve better balance between global exploration and local exploitation, the Sine chaotic map is introduced to the escape energy, replacing random components. Furthermore, an elite differential mutation strategy combined with Gaussian mutation is designed to prevent the algorithm from falling into local optima. We compared the SDHHO algorithm with other classical and novel algorithms on 23 benchmark functions, and the results demonstrated the superiority of SDHHO. The proposed algorithm is applied to the smooth path planning for mobile robots, which is transformed into an optimization problem of control nodes of high-order Bezier curves. Empirical evaluations across diverse environments underscore the proficiency of the proposed method in generating paths characterized by reduced length and enhanced safety and continuity. [ABSTRACT FROM AUTHOR]

Ship berthing is one of the most challenging operations for crews, involving optimal trajectory generation and intricate harbor maneuvering at low speed. In this paper, we present a practical path-planning method that generates smooth trajectories for an underactuated surface vehicle (USV) traveling in a confined harbor environment. Our approach introduces a Generalized Voronoi Diagram (GVD)-based path planner to handle the unberthing phase. The hybrid A* search-based path finding method is used for the transportation phase. A simple planner based on a Bézier curve is proposed for the berthing phase. To track the target path, an adaptive pure pursuit method and proportional-derivative (PD) controller is used. The performance of the given method is tested numerically and experimentally on a catamaran with a pair of non-steerable thrusters. The results demonstrate that the proposed algorithm can achieve a successful berthing operation through static obstacle handling and smooth trajectory generation. [ABSTRACT FROM AUTHOR]

The act of transmitting photos via the Internet has become a routine and significant activity. Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced. This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images. The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression. This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission. By employing scale attributes and a key-dependent parametric Long Short-Term Memory (LSTM), it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions. Furthermore, the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished. For applications involving the transmission and sharing of images across a network. The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer. An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities. This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss. This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration. The proposed system attained a Gaussian noise value of 98% and a rotation accuracy surpassing 99%. [ABSTRACT FROM AUTHOR]

A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bezier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavorably that it exhibits strong subjectivity and cumbersome selection process. To fill this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bezier curve to transform the path smoothing problem into an optimization problem. It rapidly determines the position of the optimal control point based on comprehension of constraints, e.g., path safety, curvature and kinematic restrains of the robot. The currently proposed method resolves the long-standing problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points, and lays the theoretical groundwork for smoothening the locus and path. To start with, according to the actual working conditions, the dataset corresponding to the position of the control point and the path deviation is constructed, and the neural network algorithm is used to solve the prediction model of the path deviation, so as to obtain the mapping relationship between the length and included angle of the control edge in the second-order Bezier curve and the path deviation. Subsequently, with reference to the prediction model of path deviation, a reliability evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the satisfactory control points in different environments. The currently proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion. [ABSTRACT FROM AUTHOR]

Path planning for mobile robots is a key technology in robotics. To address the issues of local optima trapping and non-smooth paths in mobile robot path planning, a novel algorithm based on the NSGA-II (Non-dominated Sorting Genetic Algorithm II) is proposed. The algorithm utilizes a search window approach for population initialization, which improves the quality of the initial population. An innovative fitness function is designed as the objective function for optimization iterations. A probability-based selection strategy is employed for population selection and optimization, enhancing the algorithm's ability to escape local minima and preventing premature convergence to suboptimal solutions. Furthermore, a path smoothing algorithm is developed by incorporating Bézier curves. By connecting multiple segments of Bézier curves, the problem of the high computational complexity associated with high-degree Bézier curves is addressed, while simultaneously achieving smooth paths. Simulation results demonstrated that the proposed path planning algorithm exhibited fewer iterations, superior path quality, and path smoothness. Compared to other methods, the proposed approach demonstrated better overall performance and practical applicability. [ABSTRACT FROM AUTHOR]

Due to the widespread use of mobile robots in various applications, the path planning problem has emerged as one of the important research topics. Path planning is defined as finding the shortest path starting from the initial point to the destination in such a way as to get rid of the obstacles it encounters. In this study, we propose a path planning algorithm based on a teaching-learning-based optimization (TLBO) algorithm with Bezier curves in a static environment with obstacles. The proposed algorithm changes the initially randomly selected control points step by step to obtain shorter Bezier curves that do not hit obstacles. We also improve the genetic algorithm-based path planning algorithm. Experimental results show that they provide better paths than other existing algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the uniqueness of the compliance equations and each notch profile, a curvature-adjustable multiple-axis flexure hinge with complex notch profiles is designed and investigated based on Bézier curve in this paper. The hinge can evolve into multiple-axis flexure hinges with single and hybrid, symmetric and asymmetric notch profiles composed of the ellipse, circle, hyperbola and parabola. In addition, analytical compliance equations in six degrees of freedom based on the Castigliano's Second Theorem are proposed. Then, a simplified notch profile classification method based on a binary quadratic implicit equation is proposed. Moreover, analytical compliance equations are validated by finite element analysis. The maximum relative error between the finite element analysis and the analytical results is 6.07%. Finally, the compliance, precision of rotation and stress are investigated based on structural parameters. The results show that the change in the rotation centre does not significantly affect the axial and bending compliance for the flexure hinge with a specific single and flush Bézier curve notch profile. Moreover, the flexure hinge with a notch profile consisting of ellipse and hyperbola has the highest rotational precision. The proposed curvature-adjustable multiple-axis flexure hinge can provide more compliance options for the design of compliant mechanisms. [ABSTRACT FROM AUTHOR]

This study proposes two curves that depict the vehicle–bridge contact force in a novel transportation system named AERORail, which is a lightweight cable-supported structure in which the rails and the prestressed cable form the load bearing system. Based on the contact force identified from a full-scale AERORail system, single and double-valley curves were obtained as the idealized contact force model for large- and small-span AERORail systems, respectively. This was achieved by utilizing the Bezier curves and the least squares method. The proposed curves were verified through a moving load model from a previous study under various spans and speeds. Moreover, the structural response of the AERORail structure under high-speed vehicle passing was explored using the idealized contact force model. The simulation results show that the proposed contact force model can predict the displacement response of 5 m and 15 m spans with a relative error of less than 5%, proving that the model can be used for dynamic analysis of AERORail. [ABSTRACT FROM AUTHOR]

We study the problem of testing a wide range of statistical hypotheses under the assumption of the sample being randomly right-censored. As an alternative to the classical approach which assumes the modification of a test statistic for complete data, we propose a novel imputation procedure. The new approach, for the first time, is completely hypothesis free which means that it does not require any modification for the application of different statistical procedures. The competitive properties are demonstrated with several goodness-of-fit tests to exponentiality, as well as the most well known two-sample tests. Finally, concluding remarks about whether it is better to impute data or to adapt statistical procedures are provided. [ABSTRACT FROM AUTHOR]

ObjectiveThis paper develops a multi-stage path planning algorithm for the auto docking operation of an underactuated surface vehicle (USV). As one of the most difficult tasks, docking requires advanced maneuvering skills. Therefore, it is necessary to generate a smooth trajectory in the berthing environment while maintaining control accuracy. MethodThe proposed method introduces a hybrid A* search algorithm in the pre-docking stage in which the well-known A* search algorithm is combined with the kinematic state space of the USV using the Reeds–Shepp curves, enabling it to produce sub-optimal paths that guarantee kinematic feasibility and low collision risk. For the docking stage, a cubic Bezier curve is introduced to represent the target path. The generated trajectories are then tested in numerical simulations wherein a mathematical model of a ship with two fixed propellers is established and verified. ResultsThe simulation and experimental results show that the path planning and tracking performance of the method are satisfactory.ConclusionThe proposed path planning method can contribute to automatic ship docking and the development of smart ships.

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

This article proposes an improved A* algorithm aimed at improving the logistics path quality of automated guided vehicles (AGVs) in digital production workshops, solving the problems of excessive path turns and long transportation time. The traditional A* algorithm is improved internally and externally. In the internal improvement process, we propose an improved node search method within the A* algorithm to avoid generating invalid paths; offer a heuristic function which uses diagonal distance instead of traditional heuristic functions to reduce the number of turns in the path; and add turning weights in the A* algorithm formula, further reducing the number of turns in the path and reducing the number of node searches. In the process of external improvement, the output path of the internally improved A* algorithm is further optimized externally by the improved forward search optimization algorithm and the Bessel curve method, which reduces path length and turns and creates a path with fewer turns and a shorter distance. The experimental results demonstrate that the internally modified A* algorithm suggested in this research performs better when compared to six conventional path planning methods. Based on the internally improved A* algorithm path, the full improved A* algorithm reduces the turning angle by approximately 69% and shortens the path by approximately 10%; based on the simulation results, the improved A* algorithm in this paper can reduce the running time of AGV and improve the logistics efficiency in the workshop. Specifically, the walking time of AGV on the improved A* algorithm path is reduced by 12s compared to the traditional A* algorithm.

Trajectory planning in the field of mobile robotics involves the generation of a trajectory to navigate a robot from a start state to a goal state. One widely employed technique involves a two-step approach: a path planner generates a path made up of piecewise linear segments with sharp turns, which are then smoothed in the trajectory generation step. In contrast, this work formulates trajectory generation as an optimization problem based on the Bézier curve, denoted as ‘BTP’, to generate the robot’s trajectory in one step. It uses a weighted objective function of trajectory length and navigation time to suit different optimization strategies while considering the robot’s kinematics and dynamics limitations. BTP adopts matrix-based formulations for all mathematical operations to enable dynamic adjustment of the degree of the Bézier curve during the optimization process, if convergence is not obtained with the current degree. Additionally, BTP guarantees that the robot’s trajectory is always within the open space identified by the robot’s sensors. The efficacy of BTP has been evaluated through simulations and real-world experimentation, including soccer games and cluttered environment scenarios. Finally, the performance is benchmarked against some of the existing trajectory planners. BTP reduced the robot’s navigation time by a minimum of 11% up to 55% compared to other tested trajectory planners, ensuring $C^{2}$ continuity rather than just $C^{1}$ continuity. Furthermore, it consistently achieved precise goal configuration, unlike the tested trajectory planners, which exhibited deviations of up to 0.6 meters.

With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although the artificial potential field (APF) method is commonly employed for path planning due to its simplicity and effectiveness, it can suffer from the local minimum problem when using gradient descent, causing the vehicle to become stuck before reaching the target. To address this issue and improve the efficiency and stability of path planning, this study proposes integrating prediction data into the APF and optimizing the control points of the quintic Bézier curve using sequential quadratic planning. The validity of the proposed method was confirmed through simulation using IPG CarMaker 12.0.1 and MATLAB/Simulink 2022b.

Addressing the automatic berthing task for vessels, this study introduces the Flow Matching Double Section Bezier Berth Method (FM-DSB) for handling downstream and upstream berthing instructions. By considering the orientation relationship between the direction of water flow and the berth, combined with berthing modes, the algorithm determines the vessel's entry angle into the berth and plans the berthing path using double-section Bezier curves. Effective control of vessel speed post-path determination is essential. Therefore, based on the response of vessels to propeller inputs, this study introduces the Berthing Path Velocity Matching Method (BPVM). The BPVM ensures speed matching along the berthing path through analysis of vessel acceleration and deceleration capabilities. Subsequently, simulation experiments are conducted to validate the planning algorithm for both long-distance and short-distance berthing. Furthermore, the feasibility and effectiveness of the berthing path are verified using a dual-loop path tracker based on the planned results. Experimental outcomes illustrate the adaptability of the proposed algorithm in planning berthing paths that align with vessel motion characteristics, effectively guiding vessels into berths through the designed dual-loop control system. [ABSTRACT FROM AUTHOR]

This paper presents two CNN-based systems for unsupervised image enhancement under non-uniform illumination. The core of the systems is constituted by the difference of a pair of CNNs. Each CNN is composed of two convolutional layers of neurons with exponential activation function and logarithmic activation function. A weighted sum of the non-reference loss functions is used to train the paired CNNs. It includes an entropy enhancement function and a Bézier loss function to ensure global and local enhancement complementarily. It also includes a white balance loss function to remove color cast in raw images, and a gradient improvement loss function to compensate for the high frequency degradation. In addition, it includes an SSIM (structural similarity index) loss functions to ensure image fidelity. In addition to the basic system, CNNOD, an augmented version called CNNOD+ is developed, which features an information fusion/combination module with a power-law network for gamma correction. The experimental results on two benchmark datasets are discussed to demonstrate that the proposed systems outperform the state-of-the-art methods in terms of enhancement quality, model complexity, and convergence efficiency. [ABSTRACT FROM AUTHOR]

Adequate mathematical description of magnetization curves is indispensable in engineering. The accuracy of the description has a significant impact on the design of electric machines and devices. The aim of this paper was to analyze the capability of Bézier curves systematically, to describe the nonlinear static magnetic properties of non-oriented electrical steels, and to compare this approach versus the established mathematical descriptions. First, analytic functions versus measurements were analyzed. The Bézier curves were then compared systematically with the most adequate analytic functions. Next, the most suitable orders of Bézier curves were determined for the approximation of nonlinear magnetic properties, where the influence of the range of the input measurement dataset on the approximation process was analyzed. Last, the extrapolation capabilities of the Bézier curves and analytic functions were evaluated. The general conclusion is that Bézier curves have adequate flexibility and significant potential for the approximation and extrapolation of nonlinear properties of non-oriented electrical steels. [ABSTRACT FROM AUTHOR]

Approximating Bézier curve by polygon is a traditional technique in computer-aided geometric design, and there are several methods for constructing such polygons. In this paper, we propose to approximate Bézier curve by the least square polygon (LSP). LSP was derived by least square fitting which minimizes the integral of approximating errors. We provide a closed formula for the vertices of LSP. Because of its optimization method, LSP has lower approximation errors than existing approximating polygons. This observation is verified by many numerical examples we tested and several typical examples are included in this paper. [ABSTRACT FROM AUTHOR]

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the problems of traditional material transfer equipment such as single working mode and inflexible adjustment of transfer path, intelligent material transfer system has gradually become a research hotspot in the field of logistics transmission. A path planning algorithm based on A* and dynamic window method was proposed for the modular material transmission platform, in order to improve its obstacle avoidance ability during the transfer process by flexibly adjusting the material transfer path. In the global path planning, the smoothness and static obstacle avoidance of the transmission path were realized by improving the A* weight function and integrating the Bezier curve and matrix interference theory. And by introducing the dynamic window method and extracting the global path key points as transition points for local path guidance of the transmission target, dynamic obstacle avoidance was realized when the path falling into local optimization was avoided. The research results showed that the path planning algorithm based on A* and dynamic window method could reduce the total global path length by 4.6% and the total path turning angle by 42.3%, while the dynamic obstacles could be effectively avoided in the local planning, which verified the rationality of the path planning algorithm.

Uncertain and ambiguous data is usually presented as a data point. When dealing with uncertain data, it is challenging to deal with incompleteness, imprecision, and incertitude; therefore, various mathematical models were developed to resolve issues concerning uncertainty points. To overcome this difficulty, a mathematical model with redefined control points characterised by three important components of the neutrosophic set was produced. The neutrosophic set can then be translated by creating models based on the neutrosophic theory and its relationships to produce control points. Hence, this paper represents a curve generated by combining neutrosophic control points with the Bezier basic functions using their relation as a Neutrosophic Bezier Curve. With an illustration example, we show how to visualise neutrosophic data sets into Neutrosophic Bezier Curve and their relationship.

[Objective]A key challenge for the harmless treatment center of sick and dead animal is to prevent secondary environmental pollution, especially during the process of transporting the animals from cold storage to intelligent treatment facilities. In order to solve this problem and achieve the intelligent equipment process of transporting sick and dead animal from storage cold storage to harmless treatment equipment in the harmless treatment center, it is necessary to conduct in-depth research on the key technical problems of path planning and autonomous walking of transport robots.[Methods]A * algorithm is mainly adopted for the robot path planning algorithm for indoor environments, but traditional A * algorithms have some problems, such as having many inflection points, poor smoothness, long calculation time, and many traversal nodes. In order to solve these problems, a path planning method for the harmless treatment of diseased and dead animal using transport robots based on the improved A algorithm was constructed, as well as a motion control method based on fuzzy proportional integral differential (PID). The Manhattan distance method was used to replace the heuristic function of the traditional A * algorithm, improving the efficiency of calculating the distance between the starting and ending points in the path planning process. Referring to the actual location of the harmless treatment site for sick and dead animal, vector cross product calculation was performed based on the vector from the starting point to the target point and the vector from the current position to the endpoint target. Additional values were added and dynamic adjustments were implemented, thereby changing the value of the heuristic function. In order to further improve the efficiency of path planning and reduce the search for nodes in the planning process, a method of adding function weights to the heuristic function was studied based on the actual situation on site, to change the weights according to different paths. When the current location node was relatively open, the search efficiency was improved by increasing the weight. When encountering situations such as corners, the weight was reduced to improve the credibility of the path. By improving the heuristic function, a driving path from the starting point to the endpoint was quickly obtained, but the resulting path was not smooth enough. Meanwhile, during the tracking process, the robot needs to accelerate and decelerate frequently to adapt to the path, resulting in energy loss. Therefore, according to the different inflection points and control points of the path, different orders of Bessel functions were introduced to smooth the planning process for the path, in order to achieve practical application results. By analyzing the kinematics of robot, the differential motion method of the track type was clarified. On this basis, a walking control algorithm for the robot based on fuzzy PID control was studied and proposed. Based on the actual operation status of the robot, the fuzzy rule conditions were recorded into a fuzzy control rule table, achieving online identification of the characteristic parameters of the robot and adjusting the angular velocity deviation of robot. When the robot controller received a fuzzy PID control signal, the angular velocity output from the control signal was converted into a motor rotation signal, which changed the motor speed on both sides of the robot to achieve differential control and adjust the steering of the robot.[Results and Discussions]Simulation experiments were conducted using the constructed environmental map obtained, verifying the effectiveness of the path planning method for the harmless treatment of sick and dead animal using the improved A algorithm. The comparative experiments between traditional A * algorithm and improved algorithm were conducted. The experimental results showed that the average traversal nodes of the improved A * algorithm decreased from 3 067 to 1 968, and the average time of the algorithm decreased from 20.34 s to 7.26 s. Through on-site experiments, the effectiveness and reliability of the algorithm were further verified. Different colors were used to identify the planned paths, and optimization comparison experiments were conducted on large angle inflection points, U-shaped inflection points, and continuous inflection points in the paths, verifying the optimization effect of the Bessel function on path smoothness. The experimental results showed that the path optimized by the Bessel function was smoother and more suitable for the walking of robot in practical scenarios. Fuzzy PID path tracking experiment results showed that the loading truck can stay close to the original route during both straight and turning driving, demonstrating the good effect of fuzzy PID on path tracking. Further experiments were conducted on the harmless treatment center to verify the effectiveness and practical application of the improved algorithm. Based on the path planning algorithm, the driving path of robot was quickly planned, and the fuzzy PID control algorithm was combined to accurately output the angular velocity, driving the robot to move. The transport robots quickly realized the planning of the transportation path, and during the driving process, could always be close to the established path, and the deviation error was maintained within a controllable range.[Conclusions]A path planning method for the harmless treatment of sick and dead animal using an transport robots based on an improved A * algorithm combined with a fuzzy PID motion control was proposed in this study. This method could effectively shorten the path planning time, reduce traversal nodes, and improve the efficiency and smoothness of path planning.

This paper presents an Enhanced Particle Swarm Optimisation (EPSO) algorithm to improve multi-robot path planning by integrating a new path planning scheme with a cubic Bezier curve trajectory smoothing algorithm. Traditional PSO algorithms often result in suboptimal paths with numerous turns, necessitating frequent stops and higher energy consumption. The proposed EPSO algorithm addresses these issues by generating smoother paths that reduce the number of turns and enhance the efficiency of multi-robot systems. The proposed algorithm was evaluated through simulations in two scenarios, and its performance was compared against the basic PSO algorithm. The results demonstrated that EPSO consistently produced shorter, smoother paths with fewer directional changes, albeit with slightly longer execution times. This improvement translates to more efficient navigation, reduced energy consumption, and enhanced overall performance of multi-robot systems. The findings underscore the potential of EPSO in applications requiring precise and efficient path planning, highlighting its contribution to advancing the field of robotics.

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development.

The filled function method has recently become very popular in optimization theory, as it is an e cient and e ective method for finding the global minimizer of multimodal functions. However, the fact that the existing filled functions in the literature generally have exponential or logarithmic terms and/or parameter sensitivity reduces the e ectiveness of this method. In this study, we propose a new non parameter and without exponential/logarithmic terms filled function, which is numerically stable, and is successfully used to solve global optimization problems. Furthermore, we have demonstrated how successful this new filled function method in terms of e ciency with numerical experiments and comparisons.

Abstract Although existing super-resolution networks based on deep learning have obtained good results, it is still challenging to achieve an ideal visual effect for irregular texts, especially spatially deformed ones. In this paper, we propose a robust Bezier Curve-based image super-resolution network (BCSR), which can efficiently handle the degradation caused by deformations. Firstly, the arbitrarily shaped text is adaptively fitted by a parameterized Bezier curve, aiming to convert a curved text box into an annotated text box. Then, we design a BezierAlign layer to calibrate between the extracted features and the input image. By importing the extracted text prior information, the accuracy of the super-resolution network can be significantly improved. It is worth highlighting that we propose a kind of text prior loss that enables the text prior image and the super-resolution text image to achieve cooperation enhancement. Extensive experiments on several standard scene text datasets demonstrate that our proposed model achieves desirable objective evaluation results and further immensely helps downstream tasks related to text recognition, especially in text instances with multi-orientation and curved shapes.