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Path tracking is an essential aspect of the navigational process of self-driving cars. Especially, accurate path tracking is important for not only normal urban roads but also narrow and complex roads such as parking lot and alleyway. The pure pursuit method is one of the geometric path-tracking methods. Using this method, the look-ahead point can be selected far away and the control input is computed in real-time, which is advantageous when the given path is not smooth or when the path is specified using waypoints. Moreover, this method is more robust to localization errors than the model-based path-tracking method. However, the original pure pursuit method and its variants have limited tracking performance. Therefore, this paper proposes a new method that heuristically selects a look-ahead point by considering the relationship between a vehicle and a path. Using this new look-ahead point, the vehicle can stably converge to the desired path and track the path without encountering the cutting-corner problem. The proposed method was tested using simulation and our self-driving car platform. Our results show that the vehicle tracks the desired path more accurately using our proposed algorithm than using the previous pure pursuit methods.

In recent years, the agricultural applications of unmanned vehicles have garnered significant attention thanks to the rapid development of global positioning systems, inertial navigation technology, and control theory. In this study, a novel sliding mode controller for farm vehicles lateral path tracking control in the presence of unknown disturbances is created. Based on the standard kinematic model and the study of agricultural circumstances, the kinematic error model with unknown external disturbances and severe nonlinearity is initially constructed. To deal with the disturbances that exist in the lateral path tracking system, this work offers a finite-time disturbance observer-based composite terminal sliding mode control (FDOB-CTSMC). Meanwhile, the finite-time disturbance observer-based composite adaptive terminal sliding mode control (FDOB-CATSMC) is developed on the basis of the sliding mode filter and the adaptive control technology, which will significantly reduce the controller chattering issue. Using the Lyapunov theory, the finite-time convergence of the lateral deviation and the sliding variable can be verified. The numerical simulations demonstrate that the proposed controller is far better than the traditional path tracking controllers.

In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Unmanned driving technology for agricultural vehicles is pivotal in advancing modern agriculture towards precision, intelligence, and sustainability. Among agricultural machinery, autonomous driving technology for agricultural tractor-trailer vehicles (ATTVs) has garnered significant attention in recent years. ATTVs comprise large implements connected to tractors through hitch points and are extensively utilized in agricultural production. The primary objective of current research focus on autonomous driving technology for tractor-trailers is to enable the tractor to follow a reference path while adhering to constraints imposed by the trailer, which may not always align with agronomic requirements. To address the challenge of path tracking for ATTVs, this paper proposes a fuzzy back-stepping path tracking controller based on the kinematic model of ATTVs. Initially, the path tracking kinematic error model was established with the trailer as the positioning center in the Frenet coordinate system using the velocity decomposition method. Then, the path tracking controller was designed using the back-stepping algorithm to calculate the target front wheel steering angle of the tractor. The gain coefficient was adaptively adjusted through a fuzzy algorithm. Co-simulation and experiments were conducted using MATLAB/Simulink/CarSim and a physical platform, respectively. Simulation results indicated that the proposed controller reduced the trailer's online time by 36.33%. When following a curved path, the trailer's tracking error was significantly lower than that of the Stanley controller designed for a single tractor. In actual experiments, while tracking a U-turn path, the proposed controller reduced the average absolute value of the trailer's path tracking lateral error by 65.27% and the maximum lateral error by 87.54%. The mean absolute error (MAE) values for lateral error and heading error were 0.010 and 0.016, respectively, while the integral of absolute error (IAE) values were 1.989 and 2.916, respectively. The proposed fuzzy back-stepping path tracking controller effectively addresses the practical challenges of ATTV path tracking. By prioritizing the path tracking performance of the trailer, the quality and efficiency of ATTVs during field operations are enhanced. The significant reduction in tracking errors and online time demonstrates the effectiveness of the proposed controller in improving the accuracy and efficiency of ATTVs.

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions.

This research proposes a novel BLF-based backstepping controller for path tracking of Autonomous Vehicles (AVs) with unknown dynamics and unmeasurable states. The proposed framework includes: (1) forming geometric-dynamic model of the vehicle by combining the dynamics of the vehicle with the kinematics of the visual measurement system, (2) designing a fixed-time Extended-State Observer (ESO) to estimate the unknown dynamics and unmeasurable states, and (3) introducing a BLF-based controller for faster response and more accurate path tracking compared to previous BLF-based controllers. Besides the novelty of the BLF-based controller, by transforming the closed-loop error dynamics into a unified proportional-derivative (PD)-type structure, an intuitive criterion is proposed to provide a systematic procedure for comparing BLF-based controllers. A combined BLF is further proposed based on this performance criterion to eliminate the sensitivity of BLF-based controllers to the magnitude of the constraint. The stability analysis is performed for the fixed-time ESO and the closed-loop control system. MATLAB/CarSim co-simulation is conducted to evaluate the performance of the proposed control system. The outcomes of the work show that the closed-loop control system is exponentially stable. In addition, it can provide a faster response and result in more accurate path tracking compared to previous BLF-based control systems.

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments.

During the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method.

•A hierarchical control framework for IWMEBs is proposed to achieve the coordinated control of path tracking and energy optimization.•A modular nonlinear observer is designed to estimate the vehicle velocity based on the employing of unknown input observer.•A novel coordinated control strategy is proposed to minimize the energy consumption of IWMEB in path tracking process.•The estimated longitudinal and lateral tire forces are obtained by direct observation rather than tire modeling.•The NMPC controller is designed, and the tracking error, energy consumption and dynamic demand of IWMEB are integrated into its objective function. Accurate velocity estimation plays a significant role in improving path tracking performance. Meanwhile, for in-wheel motor drive electric buses (IWMEBs), how to tradeoff the energy optimization and torque distribution in path tracking is also a challenging task. To address the above issues, a hierarchical control framework is proposed in this paper for IWMEBs based on nonlinear model predictive control (NMPC), under which a modular nonlinear observer is designed to achieve accurate velocity estimate. The framework can be divided into two layers. In the upper layer, a modular nonlinear observer is designed to estimate the longitudinal and lateral velocities, considering the dependence of path tracking control on vehicle states. By replacing the tire model with the estimated longitudinal and lateral forces, the errors caused by the inaccurate parameters and modeling can be reduced. In the lower layer, a coordinated control method is proposed to realize the accurate path tracking while minimizing the energy consumption based on NMPC. By integrating tracking error, energy consumption and dynamic demand of IWMEB into an objective function, the coordinated control of path tracking and energy optimization is accomplished. The comparison simulation is implemented via Trucksim-Simulink joint simulation, whose results demonstrate that the proposed hierarchical control framework is effective and its performance is satisfactory.

The integrated path tracking control of steering and differential braking can significantly improve the tracking performance of autonomous vehicles in collision avoidance in the limit conditions. However, the distribution of steering and braking control rights has not received sufficient attention in the existing control method. The distribution strategy is relatively simple and lacks theoretical support. Therefore, aiming at the problem of the distribution of steering and braking control rights in the integrated path tracking control, a tire force distribution rule is proposed in this study, and a path tracking control method based on holistic model predictive control (MPC) is designed. To describe the coupling and strong nonlinearity of tire dynamics, a UniTire tire model with combined slip conditions is established in the controller model. Furthermore, the nonlinear controller model is linearized by Taylor expansion and a linear time-varying MPC controller is designed to improve the real-time performance of the system. Finally, the effectiveness of the proposed method is verified via the co-simulation tests of CarSim and Simulink. The simulation tests at the different speeds and road friction coefficients demonstrate the superiority of the proposed method in path tracking performance, lateral stability, and traffic efficiency.