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There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges. [ABSTRACT FROM AUTHOR]

Path tracking is critical for agricultural vehicles to achieve autonomous operation and to improve operational efficiency and accuracy. This study aims to address the high-precision path tracking requirements for the tracked vehicle GY-8. An improved pure pursuit path tracking control method is proposed to enhance the performance of path tracking. For the GY-8 vehicle's single-sided braking and steering approach, a dual-wheel differential kinematic model is established. A smooth steering method using PWM (Pulse Width Modulation) is designed to reduce the likelihood of deviation from the predetermined path due to PWM braking steering. A method based on the theory of circular arc similarity is introduced to determine the path curvature and segmentation. The NSGA-II optimisation algorithm is employed to optimise and obtain the optimal look-ahead distance for different curvature segments, thereby enhancing the accuracy of path tracking. The improved algorithm was experimentally validated for path tracking on paved surfaces. In the experiments, the improved algorithm demonstrated average error, maximum error, error standard deviation, and Fréchet distance of 0.0266 m, 0.0973 m, 0.0195 m, and 0.0891 m, respectively. This represents a 15.6%, 25.8%, 4.9%, and 27.6% improvement over the pure tracking algorithm. When applying the improved pure tracking algorithm to path tracking in agricultural orchard soil environments, the results indicated maximum error, average error, and error standard deviation of 0.1272 m, 0.0351 m, and 0.0215 m, respectively. The overall findings suggest that the improved method significantly enhances the accuracy of path tracking, providing theoretical support for advancing navigation technology in tracked vehicles. • An improved path tracking control approach proposed. • Circular arc similarity method determines curvature of each path segment. • NSGA-II optimizes look-ahead distance for varying curvatures. • Experimental results show that the algorithm performs satisfactorily. [ABSTRACT FROM AUTHOR]

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The lookahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications. [ABSTRACT FROM AUTHOR]

Hierarchical rule‐base reduction (HRBR) was used to create a new form of waypoint navigation control for a skid‐steer vehicle, which consists of a multiple input‐single output nonlinear fuzzy angular velocity controller. HRBR enabled an increase in system complexity by only selecting the rules most influential on state errors. The membership functions of the fuzzy controller employed a trapezoidal structure with a completely symmetric rule‐base. The work was motivated by a desire to find the limitations of fuzzy control in terms of accuracy and robustness across environments. To accomplish this, an examination of the proposed controller is completed by employing test courses. The test courses examine the effects of steering disturbance, phase lag, and overshoot. The controller's performance was compared with existing waypoint navigation controllers, pure pursuit, and the Huskić Buck Zell (HBZ) controller. After initial controller development/tuning was completed in the simulation, the controllers were tested outdoors. The HRBR fuzzy was found to outperform the pure pursuit and HBZ in simulation (36.74% and 80.56% improvement in Root Mean Square Error (RMSE) on one course) and on concrete with mixed results on a surface with different dynamics, namely grass (23.13% and 55.64% improvement in RMSE averaging the two on the same course). If a moderate amount of tuning of the fuzzy controller had not been performed in simulation, failure across different environments would be more likely. The present study is important as it illustrates the potential effectiveness of the HRBR for control applications beyond waypoint navigation. [ABSTRACT FROM AUTHOR]

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive fuzzy pure pursuit trajectory tracking algorithm for autonomous unmanned ground vehicles (UGVs), addressing the challenges of accurate and stable navigation in complex environments. Traditional pure pursuit methods with fixed look-ahead distances struggle to maintain precision in dynamic and uneven terrains. Our approach uniquely integrates a fuzzy control algorithm that allows for real-time adjustments of the look-ahead distance based on environmental feedback, thereby enhancing tracking accuracy and smoothness. Additionally, we combine this with model predictive control (MPC) and whole-body motion control (WBC), where MPC forecasts future states and optimally adjusts control actions, while WBC ensures coordinated motion of the UGV, maintaining balance and stability, especially in rough terrains. This integration not only improves responsiveness to changing conditions but also enables dynamic balance adjustments during movement. The proposed algorithm was validated through simulations in Gazebo and real-world experiments on physical platforms. In real-world tests, our algorithm reduced the average trajectory tracking error by 45% and the standard deviation by nearly 50%, significantly improving stability and accuracy compared to traditional methods.

There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges.

This study aims to improve the working model of autonomous wheelchair navigation for disabled patients using the internet of things (IoT). A proportional-integral-derivative (PID) control algorithm is applied to the autonomous wheelchair to control movement based on position coordinates and orientation provided by the global positioning system (GPS) and digital compass sensor. This system is controlled through the IoT system, which can be operated from a web browser. Autonomous wheelchairs are handled using a waypoint algorithm; ESP8266 is used as a microcontroller unit that acts as a bridge for transmitting data obtained by sensors and controlling the direct current (DC) motors as actuators. The proposed system and the autonomous wheelchair performance gave satisfactory results with a longitude and latitude error of 1.1 meters to 4.5 meters. This error is obtained because of the limitations of GPS with the type of Ublox Neo-M8N. As a starting point for further research, a mathematical model of a wheelchair was created, and pure pursuit control algorithm was used to simulate the movement. An open-source autonomous IoT platform for electric wheelchairs has been successfully created; this platform can help nurses and caretakers. [ABSTRACT FROM AUTHOR]

Autonomous navigation in agricultural fields presents a unique challenge due to the unpredictable outdoor environment. Various approaches have been explored to tackle this task, each with its own set of challenges. These include GPS guidance, which faces availability issues and struggles to avoid obstacles, and vision guidance techniques, which are sensitive to changes in light, weeds, and crop growth. This study proposes a novel idea that combining GPS and visual navigation offers an optimal solution for autonomous navigation in agricultural fields. Three solutions for autonomous navigation in cotton fields were developed and evaluated. The first solution utilized a path tracking algorithm, Pure Pursuit, to follow GPS coordinates and guide a mobile robot. It achieved an average lateral deviation of 8.3 cm from the pre-recorded path. The second solution employed a deep learning model, specifically a fully convolutional neural network for semantic segmentation, to detect paths between cotton rows. The mobile rover then navigated using the Dynamic Window Approach (DWA) path planning algorithm, achieving an average lateral deviation of 4.8 cm from the desired path. Finally, the two solutions were integrated for a more practical approach. GPS served as a global planner to map the field, while the deep learning model and DWA acted as a local planner for navigation and real-time decision-making. This integrated solution enabled the robot to navigate between cotton rows with an average lateral distance error of 9.5 cm, offering a more practical method for autonomous navigation in cotton fields.

In this paper, for self‐driving forklift, a path‐following framework based on composite disturbance rejection that combines a cascaded active disturbance rejection controller and an online estimator of model parameters is proposed on the basis of a geometry feedforward controller. The model‐based cascaded active disturbance rejection control (ADRC), in particular, is composed of an outer‐loop controller and an inner‐loop controller, with the outer‐loop controller reducing lateral error by manipulating the desired heading direction and the inner‐loop controller achieving heading control by adjusting the steering angle. The unmodeled dynamics of the actual forklift motion are denoted as total disturbances, which can be timely estimated by the extended state observer (ESO). Moreover, to increase the transient response, a parameter self‐learning based geometry feedforward controller is designed in conjunction with the forklift characteristics. The stability analysis is presented and the effectiveness of the composite algorithm is quantitatively evaluated in experiments, demonstrating the superiority over the conventional pure pursuit algorithm and the only use of ADRC. [ABSTRACT FROM AUTHOR]

Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging because of hardware costs, the time required for preparation and tests, and constraints associated with external aspects such as meteorological or weather conditions or limited space in research laboratories. This paper proposes a methodology for the real-time hardware-in-the-loop emulation of path tracking strategies in low-cost agricultural robots. This methodology enables the real-time validation of path tracking strategies before their implementation on the robot. To validate this, we propose implementing a path tracking strategy using only the information of motor's angular speed and robot yaw velocity obtained from encoders and a low-cost inertial measurement unit (IMU), respectively. This paper provides a simulation with MATLAB/Simulink, hardware-in-the-loop with Qube-servo (Quanser), and experimental results with an Agribot platform to confirm its validity. [ABSTRACT FROM AUTHOR]

The study of pursuit curves is valuable in the context of air-to-air combat as pure pursuit guidance (heading directly at the target) is oftentimes implemented. The problems considered in this paper concern a Pursuer, implementing pure pursuit (i.e., line of sight guidance), chasing an Evader who holds course. Previous results are applicable to the case in which capture is defined as the two agents being coincident, i.e., point capture. The focus here is on obtaining results for the more realistic case where the pursuer is endowed with an effector whose range is finite. The scenario in which the Evader begins inside the Pursuer's effector range is also considered (i.e., escape from persistent surveillance, among other potential applications). Questions herein addressed include: does the engagement end in head-on collision or tail chase, will the Evader be captured or escape, what is the minimum distance the Pursuer will attain, for two Pursuers, is simultaneous capture/escape optimal and, if so, what is the optimal heading for the Evader (max time to capture, or min time to escape), and the feasibility for a fast Evader to escape from many Pursuers. Where possible, closed-form, analytic results are obtained, otherwise attention is given to computability with an eye towards real-time, on-board implementation. [ABSTRACT FROM AUTHOR]

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse. [ABSTRACT FROM AUTHOR]

Navigating a mobile robot through a curve path is a difficult task in both on-road and off-road navigation. This research provides a hardware and software platform to address this issue. Two types of research have been conducted in this paper. First, a hardware platform consisting of a Linear Actuator-based steering mechanism is implemented to hold the steering at the desired angle. Second, the curvature information from Google Maps path data is collected and used to improve the existing Pure Pursuit algorithm, which is known as the curve-aware Pure Pursuit (C-PP) algorithm, which navigates a mobile robot in both on-road and off-road (with scattered small obstacles) environments. The mobile robot is navigated using C-PP and steering is controlled by a Linear Actuator. The results taken in real-time and simulation show that the proposed algorithm for on-road and off-road environments has lower longitudinal and lateral RMSE values compared to the existing algorithms.

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results. [ABSTRACT FROM AUTHOR]

This paper proposes a path-tracking method for agricultural vehicles based on the 4WIS-4WID structure and fuzzy control to improve the operation performance of agricultural machinery in the greenhouse. The influential model of two critical parameters, α and R, on the position correction is obtained based on the relationship analysis between the traditional pure pursuit model and the vehicle structure. Based on aiming pursuit, the relationship equation between the position deviation, lateral deviation d, and heading deviation θ is established. A two-input and two-output fuzzy controller is designed, and the lateral deviation d and heading deviation θ are the input variables. Values of α and R are obtained after fuzzification, fuzzy inference, and defuzzification, and a validated MATLAB model is used to simulate different scenarios. Results of the tests show that the steady-state error of path tracking based on fuzzy control pursuit is between 35 and 51 mm, and the stability distance is between 1661 and 3052 mm for straight path tracking in four initial states. The rectangular corners have the highest inaccuracy. The results of fuzzy control pursuit show a significant improvement in path-tracking performance that can influence vehicle navigation capability in the greenhouse.

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.

In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP–PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm’s performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP–PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP–PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP–PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

In this paper, it is shown that most guidance methods can be considered as a specific form of the cross-product steering law which is based on aligning two physical vectors along with each other, and the only difference between these guidance schemes is the pair of vectors that should be aligned during the guidance process. After verifying this fact, it is concluded that the angle between these aligning vectors can be considered as a sliding surface and based on this observation, a generalised guidance structure is proposed using the sliding mode control. This approach presents a unified strategy for analysing and implementing different guidance methods such as velocity-to-be-gained, pure pursuit, proportional navigation and path following. The main advantages of this structure are its generality and simple extension to the three-dimensional space. Finally, the superiority of the proposed approach to traditional strategies is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

Focusing on the problems of big overshoot and long convergence time due to the large initial heading error, a new path tracking control strategy for autonomous harvesting robots based on improved double arc path planning is designed. Firstly, the improved double arc path planning algorithm includes global path planning and local planning. The shortest path distance is taken as the goal, so the optimal tangent arc path from the initial point to the reference straight line can be automatically planned by the global path planning. Due to the problems such as control delay in harvesting robot, the global path cannot be effectively tracked. By analyzing the influence of the non-mutable steering angle and control delay on path tracking, a new local path planning algorithm is proposed. Then, the two preview points obtained in global planning can be optimized dynamically, and the actual reliability of the algorithm can be enhanced. Secondly, the error compensation model is designed to compensate the installation error of the positioning antenna in real time, to satisfy the simplified condition of the two-wheeled vehicle model. Finally, by combining the large angle steering control method and the pure pursuit algorithm, a new hybrid control strategy is designed to solve the steering angle, and the path tracking function of harvesting robots is realized. Experiment results demonstrate that the hybrid control strategy can reduce the oscillation of driving path, enhance the convergence, then, improve working quality and efficiency of robots. [ABSTRACT FROM AUTHOR]

As the development of multidisciplinary techniques and ever-growing demands, autonomous system has been widely investigated in a variety of fields. In recent two decades, autonomous surface vessels have gained increasing attention from both industry and research communities, previous related work mainly focuses on the design of maneuver controllers, most algorithms are under some specific assumptions and always involve too many parameters that need to be tuned, leading to the lack of practical application or validation in real autonomous surface vessel platform. To tackle the issues, an integrated autonomous surface vessel prototype is designed and implemented, both navigation and maneuver control subsystems are improved, especially in terms of autonomous control, a simple geometrical model and constrained pure pursuit algorithm is firstly tried in autonomous surface vessel, the results show that the proposed system can achieve the path tracking with the error smaller than 40 cm, and even given a complicated waypoint mission, it can still maintain the accuracy of path tracking, demonstrating the stability and validity of the autonomous surface vessel prototype. [ABSTRACT FROM AUTHOR]

Featured Application: This work is used for driverless vehicles, especially for long wheelbase vehicles. Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus's lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car's forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods. [ABSTRACT FROM AUTHOR]

• The navigation system can handle the navigation tasks of overall orchards. • Two integrated modes improve the robustness of the overall navigation. • Constant offset-keeping navigation improves the field task adaptivity. • The refined pure pursuit method has good adaptivity on path curvature and velocity. • Overall orchard navigation conducts easily in accordance with the CP value. Overall navigation in orchards is essential for autonomous production activities, however, most navigation studies, in orchards, focus on in-row scenes, and few of them pay attention to the headland transition or the overall navigation. This paper proposes an integrated navigation method based on satellite and lidar for overall orchards scenes, where the lidar was mainly used for in-row navigation and chassis position (CP) obtaining, whilst the satellite was utilized for headland transition as well as the assistant for in-row integrated navigation. Two overall integrated navigation modes were investigated: CMD-INT, which integrates the command signals from the lidar-based and satellite-based navigation systems, and BIAS-FUS, which combines the information of bias from the two navigation systems. The CP was used to detect the headland of the tree rows and achieve overall navigation integration. A position-ratio-based path planning method was also proposed, which enabled the chassis to maintain a proper constant offset from one side of the orchard. The refined pure pursuit algorithm was adopted for path tracking with good adaptivity on both the path curvature and tracking velocity. Both simulation and field tests with ten group parameters configuration in total were conducted to verify the performance of the system. The BIAS-FUS mode outperformed the CMD-INT mode, and the findings for the simulation experiments were consistent with that of the field tests. In the simulation experiments, the maximum value (Max), mean value (Mean), and root mean square error (RMSE) for the in-row process decreased by 0.0054 m, 0.0053 m, and 0.0062 m, respectively. Additionally, the corresponding heading deviation decreased by 0.3027°, 0.0050°, and 0.0256°, respectively. In the field tests, the Mean of the heading and lateral deviation for the in-row tracking process decreased by 0.3823°, and −0.0072 m respectively, and the corresponding deviations for the transition tracking process decreased by 2.8903°, and 0.0164 m respectively. Aiming at the standardized tall spindle apple orchards, this work contributes to an available overall integrated navigation method. [ABSTRACT FROM AUTHOR]

Currently, since the model of a driverless bus is not clear, it is difficult for most traditional path tracking methods to achieve a trade-off between accuracy and stability, especially in the case of driverless buses. In terms of solving this problem, a path-tracking controller based on a Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. Firstly, the reference point of Pure Pursuit is moved from the rear axle to the front axle. It relieves the influence caused by the ignorance of the bus’s lateral dynamic characteristics and improves the stability of Pure Pursuit. Secondly, a fuzzy parameter self-tuning method is applied to improve the accuracy and robustness of the path-tracking controller. Thirdly, a feedback-feedforward control algorithm is devised for velocity control, which enhances the velocity tracking efficiency. The proportional-integral (PI) controller is indicated for feedback control, and the gravity acceleration component in the car’s forward direction is used in feedforward control. Finally, a series of experiments is conducted to illustrate the excellent performances of proposed methods.

Bird's formation flight is one of the best type of cooperation in nature. The bird's flight was the motivation of humans to flight. After one century of flight development, bird's formation flight was the motivation of humans to aircraft formation flight, as well. Based on the closeness of aircrafts in formation flight and the effect of disturbances such as vortex, the guidance and control of the followers aircraft is a challenging issue. This paper introduces a novel integration between guidance commands and system controller inputs. In recent papers the control system inputs were derived from approximate equations, and this approximation caused maneuver limitation. To tackle this problem, a new method is introduced which employs PID controller in integration block. This integrated guidance and control system employs the pure pursuit guidance to determine the unmanned aerial vehicle's (UAV’s) acceleration command. A two loop dynamic inversion technique is used for designing attitude and velocity controller, while the acceleration feedback control is used between guidance system and attitude controller, which leads to increase maneuverability of UAV’s formation flight. The simulation results show that the proposed method can control the UAV's formation with sufficient accuracy in severe maneuvers.

The aerodynamic characteristics of nine configurations of supersonic continuous deflectable nose guided missiles have been investigated. The optimized geometry was achieved based on the accuracy of confliction with constant target in ground to ground mission. The studied configurations consist of a spherical nose tip, a tangent ogive, one set of stabilizing tail fines and a cylindrical body whose midsection is flexible to form an arc of a circle. So the cylindrical body consists of a fixed part in the vicinity of the nose, middle flexible part and main body with stabilizers. The effects of fixed length (Fix = 0, 1.5, 3Cal) and flexible length (Flex = 1, 2.5, 5Cal) parameters on the aerodynamics and flight dynamics of guided missile have been studied. A code has been developed to solve full Navier-Stokes equations using finite volume and Runge-Kutta time stepping techniques and modified Baldwin-Lomax turbulence model. Multi-block technique was also used to solve the main body and fin parts flow fields. Further, a 3 degree of freedom code along with a pure pursuit guidance subroutine was developed to compare planar flight dynamics of missiles. It was found that although the missiles with bigger lengths for fixed and flex parts show more maneuverability, but this is not favorable for all missile missions as sometimes it decreases the confliction accuracy. Flight dynamic analysis shows that a change in initial launch angle may shift the favorite configuration. This means only the aerodynamic defined aim functions cannot completely supersede flight dynamic analysis in geometric optimization. Further, the thrust vector moment is an important portion of total control moment as it enhances the hitting accuracy and also decreases the importance of geometry. [ABSTRACT FROM AUTHOR]

Bird's formation flight is one of the best types of cooperation in nature. The bird's flight was the motivation of humans for flying. After one century of flight development, bird's formation flight was the motivation of humans for aircraft's formation flight. The closeness of aircrafts in formation flight and the effect of disturbances such as vortex make the formation flight control a challenging issue for control designers. This paper introduces a novel integration between guidance commands and system controller inputs. In recent papers the control system inputs were derived from approximate equations, and this approximation caused maneuver limitation. To tackle this problem, a new method is introduced, which employs proportional-integral-derivative (PID) controller in the integration block. This integrated guidance and control system employs the pure pursuit guidance to determine the unmanned aerial vehicle's acceleration command. A two-loop dynamic inversion technique is used for designing attitude and velocity controller, while the acceleration feedback control is used between the guidance system and attitude controller, which leads to increase in maneuverability of unmanned aerial vehicle's formation flight. The simulation results show that the proposed method can control the UAV's formation with sufficient accuracy in severe maneuvers. [ABSTRACT FROM AUTHOR]

This article presents a sensor-based navigation algorithm for 3-degree-of-freedom car-like robots with a nonholonomic constraint. Similar to the classical Bug family algorithms, the proposed algorithm enables the car-like robot to navigate in a completely unknown environment by only using range sensor information. The car-like robot uses the local range sensor view to determine the local path so that it moves toward the goal. To guarantee that the car-like robot can approach the goal, the two modes of motion are repeated, termed as motion-to-goal and wall-following. The motion-to-goal behavior lets the robot directly move toward the goal and the wall-following behavior makes the car-like robot circumnavigate the obstacle boundary until it meets the leaving condition. For each behavior, the nonholonomic motion for the car-like robot is planned in terms of the turning radius at each step. The proposed algorithm is implemented with a real robot and the experimental results show the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Trajectory planning is one of the key technologies for autonomous driving. A* algorithm is a classical trajectory planning algorithm that has good results in the field of robot path planning. However, there are still some practical problems to be solved when the algorithm is applied to vehicles, such as the algorithm fails to consider the vehicle contours, the planned path is not smooth, and it lacks speed planning. In order to solve these problems, this paper proposes a path processing method and a path tracking method for the A* algorithm. First, the method of configuring safe redundancy space is given considering the vehicle contour, then, the path is generated based on A* algorithm and smoothed using Bessel curve, and the speed is planned based on the curvature of the path. The trajectory tracking algorithm in this paper is based on an expert system and pure tracking theory. In terms of speed tracking, an expert system for the acceleration characteristics of the vehicle is constructed and used as a priori information for speed control, and good results are obtained. In terms of path tracking, the required steering wheel angle is calculated based on pure tracking theory, and the influence factor of speed on steering is obtained from test data, based on which the steering wheel angle is corrected and the accuracy of path tracking is improved. In addition, this paper proposes a target point selection method for the pure tracking algorithm to improve the stability of vehicle directional control. Finally, a simulation analysis of the proposed method is performed. The results show that the method can improve the applicability of the A* algorithm in automated vehicle planning.