Search Results (104)

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article

Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

Realizing path tracking is crucial for improving the accuracy and efficiency of unmanned vehicle operations. In this paper, a path tracking hierarchical control method based on DDPG-PP is proposed to improve the path tracking accuracy of tracked unmanned vehicles. Constrained by the objective of minimizing path tracking error, with the upper controller, we adopted the DDPG method to construct an adaptive look-ahead distance optimizer in which the look-ahead distance was dynamically adjusted in real-time using a reinforcement learning strategy. Meanwhile, reinforcement learning training was carried out with randomly generated paths to improve the model’s generalization ability. Based on the optimal look-ahead distance output from the upper layer, the lower layer realizes precise closed-loop control of torque, required for steering, based on the PP method. Simulation results show that the path tracking accuracy of the proposed method is better than that of the LQR and PP methods. The proposed method reduces the average tracking error by 94.0% and 79.2% and the average heading error by 80.4% and 65.0% under complex paths compared to the LQR and PP methods, respectively. Full article

The motion control of a tracked mobile robot remains an important capability for autonomous navigation. Kinematic path-tracking algorithms are commonly used in mobile robotics due to their ease of implementation and real-time computational cost advantage. This paper integrates an extended Kalman filter (EKF) into a common kinematic controller for path-tracking performance improvement. The extended Kalman filter estimates the instantaneous center of rotation (ICR) of tracks using the sensor readings of GPS and IMU. These ICR estimations are then given as input to the motion control algorithm to generate the track velocity demands. The platform to be controlled is a heavyweight off-road tracked vehicle, which necessitates the investigation of slip values. A high-fidelity simulation model, which is verified with field tests, is used as the plant in the path-tracking simulations. The performance of the filter and the algorithm is also demonstrated in field tests on a stabilized road. The field results show that the proposed estimation increases the path-tracking accuracy significantly (about 44%) compared to the classical pure pursuit. Full article

As a core component of agricultural machinery autonomous navigation, path tracking control holds significant research value. The pure pursuit algorithm has become a prevalent method for agricultural vehicle navigation due to its effectiveness at low speeds, yet its performance critically depends on the selection of the look-ahead distance. The conventional approaches require extensive parameter tuning due to the complex influencing factors, while fixed look-ahead distances struggle to balance the tracking accuracy and adaptability. Considerable effort is required to fine-tune the system to achieve optimal performance, which directly affects the accuracy of the path tracking and the results in the cumbersome task of selecting an appropriate goal point for the tracking path. To address these challenges, this paper introduces a pure pursuit algorithm for high-clearance sprayers in agricultural machinery, utilizing a self-adjusting look-ahead distance. By developing a kinematic model of the pure pursuit algorithm for agricultural machinery, an evaluation function is then employed to estimate the pose of the machinery and identify the corresponding optimal look-ahead distance within the designated area. This is done based on the principle of minimizing the overall error, enabling the dynamic and adaptive optimization of the look-ahead distance within the pure pursuit algorithm. Finally, this algorithm was verified in simulations and bumpy field tests under various different conditions, with the average value of the lateral error reduced by more than 0.06 m and the tuning steps also significantly reduced compared to the fixed look-ahead distance in field tests. The tracking accuracy has been improved and the applicability of the algorithm for rapid deployment has been enhanced. Full article

A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on the prototype’s speed and pose deviation. Subsequently, an improved sparrow search algorithm (ISSA) is employed to adjust the pure pursuit model’s speed and look-ahead distance dynamically. Finally, improvements are made to the initialization of the original algorithm and the position update method between different populations. Simulation results indicate that the improved sparrow search algorithm exhibits faster convergence speed and better capability to escape local extrema. The real vehicle test results show that the proposed algorithm achieves an average lateral deviation of approximately 3 cm, an average heading deviation below 5°, an average stabilization distance under 5 m, and an average navigation time of around 46 s during path tracking. These results represent reductions of 51.25%, 30.62%, 49.41%, and 10.67%, respectively, compared to the traditional pure pursuit model. Compared to the pure pursuit model that only dynamically adjusts the look-ahead distance, the proposed algorithm shows reductions of 34.11%, 24.96%, 32.13%, and 11.23%, respectively. These metrics demonstrate significant improvements in path-tracking accuracy, pose correction speed, and path-tracking efficiency, indicating that the proposed algorithm can serve as a valuable reference for path-tracking research in complex agricultural environments. Full article

Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11–0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the shortest and most stable execution time, the MPC algorithm has a relatively longer execution time, and the PP algorithm’s execution time varies at different speeds. Through this simulation, if high control accuracy and stability are pursued, the MPC or LQR algorithm can be considered; if real-time performance and computational efficiency are more important, the PP algorithm can be considered. Full article

With the rise of the sharing economy and the concept of the sharing city, the field of urban renewal is facing new opportunities and challenges. This paper innovatively explores built heritage renewal in central Shanghai from the perspective of the sharing economy, focusing on the evolution of plot morphology and associated design strategies. Six representative cases, selected within the framework of three urban renewal policies from 1999 to the present, are analyzed using a diachronic method based on the Conzen school and the street frontage index. Combined with historical maps, aerial photographs, and satellite images, the paper analyzes the changes in plot morphology from 1999 to 2024. The paper highlights how the introduction of sharing city principles significantly impacted plot morphology, facilitating the expansion and diversification of space use and driving the restructuring of plot boundaries, including physical, property, and activity boundaries. The study further reveals how the shared city concept has led to the emergence of privately owned public spaces. Additionally, the paper discusses the pursuit of flow, openness, and sharing in urban renewal, noting how these factors have shifted the focus from purely rentable and sellable areas to more efficient space resource allocation, optimizing spatial configurations. Finally, the paper introduces the concept of “sharing by transfer”, proposing that adjustments to plot boundaries under the sharing economy framework can foster more equitable, efficient, and sustainable urban renewal, providing new perspectives and strategic recommendations for built heritage renewal. Full article

In recent decades, missile guidance and control have advanced significantly, with methods like pure pursuit (PP), command to line-of-sight (CLOS), and proportional navigation (PN) enabling accurate target interception in uncertain environments through line-of-sight (LOS) tracking. In this work, we propose a novel 3D sliding pure pursuit guidance (3DSPP) law for controlling a surface-to-air missile against a maneuvering target. The algorithm is compared with established guidance laws such as zero-effort miss distance “ZEM-PN” and “3D-PP”, with performance metrics including the miss distance $M_d$ and time of closest approach $t_{cap}$. The results demonstrate that the 3DSPP outperforms the conventional methods by achieving the lowest $M_d=$ 0.1497 m and the fastest $t_{cap}=$ 7.3853 s, ensuring more precise and rapid interception. The algorithm also exhibits superior robustness to noise and efficient energy management, making it a promising solution for real-world missile guidance systems. Full article

Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations. Full article

The transition to clean energy has accelerated the pursuit of hydrogen as a sustainable fuel. Among various production methods, proton exchange membrane electrolysis cells (PEMECs) stand out due to their ability to generate ultra-pure hydrogen with efficiencies exceeding 80% and current densities reaching 2 A/cm². Their compact design and rapid response to dynamic energy inputs make them ideal for integration with renewable energy sources. This review provides a comprehensive assessment of PEMEC technology, covering key internal components, system configurations, and efficiency improvements. The role of catalyst optimization, membrane advancements, and electrode architectures in enhancing performance is critically analyzed. Additionally, we examine state-of-the-art numerical modelling, comparing zero-dimensional to three-dimensional simulations and single-phase to two-phase flow dynamics. The impact of oxygen evolution and bubble dynamics on mass transport and performance is highlighted. Recent studies indicate that optimized electrode architectures can enhance mass transport efficiency by up to 20%, significantly improving PEMEC operation. Advancements in two-phase flow simulations are crucial for capturing multiphase transport effects, such as phase separation, electrolyte transport, and membrane hydration. However, challenges persist, including high catalyst costs, durability concerns, and scalable system designs. To address these, this review explores non-precious metal catalysts, nanostructured membranes, and machine-learning-assisted simulations, which have demonstrated cost reductions of up to 50% while maintaining electrochemical performance. Future research should integrate experimental validation with computational modelling to improve predictive accuracy and real-world performance. Addressing system control strategies for stable PEMEC operation under variable renewable energy conditions is essential for large-scale deployment. This review serves as a roadmap for future research, guiding the development of more efficient, durable, and economically viable PEM electrolyzers for green hydrogen production. Full article

Efficiency assessment is a pivotal instrument in the pursuit of sustainable operations. It is imperative to evaluate government-funded afforestation initiatives to ensure the optimal utilisation of resources, thereby enhancing sustainability. In this study, a framework for measuring afforestation efficiency at the sub-compartment scale was established based on a Bootstrap-modified Data Envelopment Analysis (DEA) model. The empirical study included 48 afforestation sub-compartments from six districts involved in the Beijing Plains Afforestation Project. The results of the study indicate that the efficiency of the afforestation sub-compartment has much room for improvement and significant individual differences. The mean scores for comprehensive efficiency, pure technical efficiency, and scale efficiency of the sample sub-compartments were 0.646, 0.664, and 0.973, respectively. Compared to the pure technical efficiency, the scale efficiency is higher. Notably, prioritising native or climate-resilient species, adopting long-term ecological maintenance protocols, and fostering financially self-sustaining mechanisms were identified as key drivers for boosting efficiency. These findings underscore the need to embed sustainability principles—including resource optimisation and economic viability—into afforestation planning and governance to strengthen ecological restoration resilience and long-term project continuity. Full article

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations. Full article

Developing stable and effective catalysts for the hydrogen evolution reaction (HER) has been a long-standing pursuit. In this work, we propose a series of single-atom catalysts (SACs) by importing transition-metal atoms into the carbon and vanadium vacancies of tetragonal V₂C₂ and V₃C₃ slabs, where the transition-metal atoms refer to Ti, V, Cr, Mn, Fe, Co, Ni, and Cu. By means of first-principles computations, the possibility of applying these SACs in HER catalysis was investigated. All the SACs are conductive, which is favorable to charge transfer during HER. The Gibbs free energy change (ΔG_H*) during hydrogen adsorption was adopted to assess their catalytic ability. For the V₂C₂-based SACs with V, Cr, Mn, Fe, Ni, and Cu located at the carbon vacancy, excellent HER catalytic performance was achieved, with a |ΔG_H*| smaller than 0.2 eV. Among the V₃C₃-based SACs, apart from the SAC with Mn located at the carbon vacancy, all the SACs can act as outstanding HER catalysts. According to the ΔG_H*, these excellent V₂C₂- and V₃C₃-based SACs are comparable to the best-known Pt-based HER catalysts. However, it should be noted that the V₂C₂ and V₃C₃ slabs have not been successfully synthesized in the laboratory, leading to a pure investigation without practical application in this work. Full article