Search Results (1,541)

In complex 3D maneuvering tasks (e.g., post-disaster rescue, urban operations, and infrastructure inspection), the trajectories that quadrotors need to track are often complex—containing both gentle flight phases and highly maneuverable trajectory segments. Under such trajectory tracking tasks with the composite characteristics of “gentle-high maneuvering”, quadrotors face challenges of limited onboard computing resources and short endurance, requiring a balance between trajectory tracking accuracy, computational efficiency, and energy consumption. To address this problem, this paper proposes a lightweight trajectory tracking control method based on hierarchical decision-making and dual-threshold adaptive switching. Inspired by the biological “prediction–reflection” mechanism, this method designs a dual-threshold collaborative early warning switching architecture of “prediction layer–confirmation layer”: The prediction layer dynamically assesses potential risks based on trajectory curvature and jerk, while the confirmation layer confirms in real time the stability risks through an attitude-angular velocity composite index. Only when both exceed the thresholds, it switches from low-energy-consuming Euler angle control to high-precision geometric control. Simulation experiments show that in four typical trajectories (straight-line rapid turn, high-speed S-shaped, anti-interference composite, and narrow space figure-eight), compared with pure geometric control, this method reduces position error by 19.5%, decreases energy consumption by 45.9%, and shortens CPU time by 28%. This study not only optimizes device performance by improving trajectory tracking accuracy while reducing onboard computational load, but also reduces energy consumption to extend UAV endurance, and simultaneously enhances anti-disturbance capability, thereby improving its operational capability to respond to emergencies in complex environments. Overall, this study provides a feasible solution for the efficient and safe flight of resource-constrained onboard platforms in multi-scenario complex environments in the future and has broad application and expansion potential. Full article

Planar underactuated manipulators are widely used in aerospace and deep-sea exploration. Due to their passive joints without actuation, it is difficult to perform trajectory planning and direct control. In this paper, an angular velocity trajectory planning method is proposed, aiming to calculate the velocity trajectory by using a superposition of basis functions and transforming the planning problem into an optimization problem by using a metaheuristic algorithm. According to the underactuated constraint relationship, the planning trajectory of the passive joint is obtained, which ensures that the passive joint can be indirectly controlled to reach the target angle while tracking the trajectory of the active joint. For the tracking control problem, a sliding mode controller with an exponential convergence rate is used to track the trajectory. Lastly, three sets of simulations are run to demonstrate the efficacy of the suggested approach. Full article

This paper presents an output-feedback sliding-mode control strategy for a two-DOF lower-limb exoskeleton system aimed at rehabilitation assistance for disabled individuals. The core of the approach is a cascade super-twisting observer, beginning with a super-twisting differentiator (STD) that estimates unmeasured angular velocities from measured joint angles. These velocity estimates feed into a second-stage adaptive super-twisting sliding-mode observer (ASTSMO), which accurately reconstructs external load torque disturbances affecting the system. Using these estimates, a sliding-mode controller robustly tracks the exoskeleton’s desired trajectories despite external disturbances. The stability of the proposed control scheme is rigorously established through Lyapunov-based analysis within a sliding-mode framework. Numerical simulations conducted in Matlab R2022/Simulink demonstrate the method’s effectiveness in accurately estimating unmeasured states and unknown disturbances, as well as achieving robust tracking performance in the presence of system uncertainties. Full article

The trajectory-tracking capability of autonomous mining trucks is critical for accomplishing transportation tasks efficiently. However, due to the diverse road surfaces and rugged terrains in open-pit mines, the existing vehicle dynamics models struggle to accurately capture the complex tire–ground interactions. As a result, conventional trajectory-tracking control methods that rely on linear vehicle dynamics models suffer from degraded tracking performance. To this end, this paper proposes a novel trajectory-tracking control framework that integrates model predictive control (MPC) with model-free adaptive control (MFAC). A warm-start strategy is employed to improve the computational efficiency of MPC, while MFAC is utilized to provide real-time compensation for the control deviations generated by MPC during the trajectory-tracking process. To validate the effectiveness of the proposed trajectory-tracking control method, co-simulations were conducted on the CarSim and MATLAB/Simulink platforms under various road conditions and driving scenarios. Simulation results demonstrate that the proposed method can effectively enhance the trajectory-tracking performance of autonomous mining trucks. For instance, under the S-condition with Class E road elevation, the proposed method achieves improvements of approximately 90.83%, 15.05%, and 71.93% in the mean error, maximum error, and root mean square error (RMSE), respectively, compared with the conventional LQR-based trajectory-tracking control method. In addition, the computation time of MPC is only 2 ms, which significantly improves the overall performance of the trajectory-tracking controller. Full article

This paper presents a kinematics-constrained grid-based path planning algorithm that generates real-time, safe, and executable trajectories, thereby enhancing the performance and reliability of autonomous vehicle parking systems. The grid resolution adapts to the minimum turning radius and steering limits, ensuring feasible motion primitives. The cost function integrates path efficiency, direction-switching penalties, and collision risk to ensure smooth and feasible maneuvers. A cubic spline refinement produces curvature-continuous trajectories suitable for vehicle execution. Simulation and experimental results demonstrate that the proposed method achieves collision-free and curvature-bounded paths with significantly reduced computation time and improved maneuver smoothness compared with conventional A* and Hybrid A*. In both structured and dynamic parking environments, the planner consistently maintained safe clearance and stable tracking performance under variations in vehicle geometry and velocity. These results confirm the robustness and real-time feasibility of the proposed approach, effectively unifying kinematic feasibility, safety, and computational efficiency for practical autonomous parking systems. Full article

With the rapid growth of autonomous vehicle technologies, effective path-tracking control has become a critical component in ensuring safety and efficiency in complex traffic scenarios. When a high-level decision-making agent generates a collision-free path, a robust low-level controller is required to precisely follow this trajectory. However, connected autonomous vehicles (CAV) are inherently affected by communication delays and computation delays, which significantly degrade the performance of conventional controllers such as PID or other more advanced controllers like disturbance observers (DOB). While DOB-based designs have shown effectiveness in rejecting disturbances under nominal conditions, their performance deteriorates considerably in the presence of unknown time delays. To address this challenge, this paper proposes a delay-tolerant communication disturbance observer (CDOB) framework for path-tracking control in delayed systems. The proposed CDOB compensates for the adverse effects of time delays, maintaining accurate trajectory tracking even under uncertain and varying delay conditions. It is shown through a simulation study that the proposed control architecture maintains close alignment with the reference trajectory across various scenarios, including single-lane change, double-lane change, and Elastic Band-generated collision avoidance paths under various time delays. Simulation results further demonstrate that the proposed method outperforms conventional approaches in both tracking accuracy and delay robustness, making it well-suited for connected autonomous driving applications. Full article

Cooperative longitudinal–lateral trajectory optimization is essential for unmanned ground vehicle (UGV) platoons to improve safety, capacity, and efficiency. However, existing approaches often face unstable formation under low penetration rates and rely on fragmented control strategies. This study develops a cooperative longitudinal–lateral trajectory tracking framework tailored for UGV platooning, embedded in a hierarchical control architecture. Dual-stage multi-objective Model Predictive Control (MPC) is proposed, decomposing trajectory planning into pursuit and platooning phases. Each stage employs adaptive weighting to balance platoon efficiency and traffic performance across varying operating conditions. Furthermore, a traffic-aware organizational module is designed to enable the dynamic opening of UGV-dedicated lanes, ensuring that platoon formation remains compatible with overall traffic flow. Simulation results demonstrate that the adaptive weighting strategy reduces the platoon formation time by 41.6% with only a 1.29% reduction in the average traffic speed. In addition, the dynamic lane management mechanism yields longer and more stable UGV platoons under different penetration levels, particularly in high-flow environments. The proposed cooperative framework provides a scalable solution for advancing UGV platoon control and demonstrates the potential of unmanned systems in future intelligent transportation applications. Full article

This paper presents a hybrid reinforcement learning framework for trajectory tracking control of a 5-degree-of-freedom (DOF) Mitsubishi RV-2AJ robotic arm by integrating model-free deep reinforcement learning (DRL) algorithms with classical control strategies. A novel hybrid PID + TD3 agent is proposed, combining a Proportional–Integral–Derivative (PID) controller with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and is compared against standalone TD3 and PID controllers. In this architecture, the PID controller provides baseline stability and deterministic disturbance rejection, while the TD3 agent learns residual corrections to enhance tracking accuracy, robustness, and control smoothness. The robotic system is modeled in MATLAB/Simulink with Simscape Multibody, and the agents are trained using a reward function inspired by artificial potential fields, promoting energy-efficient and precise motion. Extensive simulations are performed under internal disturbances (e.g., joint friction variations, payload changes) and external disturbances (e.g., unexpected forces, environmental interactions). Results demonstrate that the hybrid PID + TD3 approach outperforms both standalone TD3 and PID controllers in convergence speed, tracking precision, and disturbance rejection. This study highlights the effectiveness of combining reinforcement learning with classical control for intelligent, robust, and resilient robotic manipulation in uncertain environments. Full article

This paper addresses the challenges of inadequate trajectory tracking accuracy and limited parameter adaptability encountered by hip joints in lower-limb exoskeletons operating across multi-terrain environments. To mitigate these issues, we propose a hybrid control strategy that synergistically combines the slime mould algorithm (SMA) with fuzzy PID control, thereby improving the trajectory tracking performance in such diverse conditions. Initially, we established a dynamic model of the hip joint in the sagittal plane utilizing the Lagrange method, which elucidates the underlying motion mechanisms involved. Subsequently, we designed a fuzzy PID controller that facilitates dynamic parameter adjustment. The integration of the slime mould algorithm (SMA) allows for the optimization of both the quantization factor and the proportional factor of the fuzzy PID controller, culminating in the development of a hybrid control framework that significantly enhances parameter adaptability. Ultimately, we performed a comparative analysis of this hybrid control strategy against conventional PID, fuzzy PID, and PSO-fuzzy PID controls through MATLABR2023b/Simulink simulations as well as experimental tests across a range of multi-terrain scenarios including flat ground, inclines, and stair climbing. The results indicate that in comparison to PID, fuzzy PID, and PSO-fuzzy PID controls, our proposed strategy significantly reduced the adjustment time by 15.06% to 61.9% and minimized the maximum error by 39.44% to 72.81% across various terrains including flat ground, slope navigation, and stair climbing scenarios. Additionally, it lowered the steady-state error ranges by an impressive 50.67% to 90.75%. This enhancement markedly improved the system’s response speed, tracking accuracy, and stability, thereby offering a robust solution for the practical application of lower-limb exoskeletons. Full article

In ocean engineering, path following serves as a fundamental capability for autonomous underwater vehicles (AUVs), enabling essential operations such as environmental exploration and inspection. However, for robotic dolphins employing dorsoventral undulatory propulsion, the periodic pitching induces strong coupling between propulsion and attitude, posing significant challenges for precise path following in disturbed environments. In this paper, a real-time robust path-following control framework is proposed for robotic dolphins to address these challenges. First, a novel robotic dolphin platform is presented by integrating a dorsoventral propulsion mechanism with a passive peduncle joint, followed by the systematic formulation of a full-state dynamic model. Then, a minimum-snap-based path optimizer is constructed to generate smooth and dynamically feasible trajectories, improving path quality and motion safety. Subsequently, a robust model predictive controller is developed, which incorporates control surface dynamics, a nonlinear disturbance observer, and a Sigmoid-based disturbance-grading mechanism to ensure fast attitude response and precise tracking performance. Finally, extensive simulations under various environmental disturbances validate the effectiveness of the proposed approach in both trajectory optimization and robust path following. The proposed framework not only demonstrates strong robustness in path following and disturbance rejection, but also provides practical guidance for future underwater missions such as long-term environmental monitoring, inspection, and rescue. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

We present a visual–inertial odometry (VIO) system that integrates a deep feature extraction and filtering strategy with optical flow to improve tracking robustness. While many traditional VIO methods rely on hand-crafted features, they often struggle to remain robust under challenging visual conditions, such as low texture, motion blur, or lighting variation. These methods tend to exhibit large performance variance across different environments, primarily due to the limited repeatability and adaptability of hand-crafted keypoints. In contrast, learning-based features offer richer representations and can generalize across diverse domains thanks to data-driven training. However, they often suffer from uneven spatial distribution and temporal instability, which can degrade tracking performance. To address these issues, we propose a hybrid front-end that combines a lightweight deep feature extractor with an image pyramid and grid-based keypoint sampling to enhance spatial diversity. Additionally, a forward–backward optical-flow-consistency check is applied to filter unstable keypoints. The system improves feature tracking stability by enforcing spatial and temporal consistency while maintaining real-time efficiency. Finally, the effectiveness of the proposed VIO system is validated on the EuRoC MAV benchmark, showing a 19.35% reduction in trajectory RMSE and improved consistency across multiple sequences compared with previous methods. Full article

This study advances the unmanned aerial vehicle (UAV) localization technology within the framework of a low-altitude economy, with particular emphasis on the accurate and real-time identification and tracking of unauthorized (“black-flying”) drones. Conventional YOLOv8s-based target detection algorithms often suffer from missed detections due to their reliance on single-frame features. To address this limitation, this paper proposes an improved detection algorithm that integrates a long-short-term memory (LSTM) network into the YOLOv8s framework. By incorporating time-series modeling, the LSTM module enables the retention of historical features and dynamic prediction of UAV trajectories. The loss function combines bounding box regression loss with binary cross-entropy and is optimized using the Adam algorithm to enhance training convergence. The training data distribution is validated through Monte Carlo random sampling, which improves the model’s generalization to complex scenes. Simulation results demonstrate that the proposed method significantly enhances UAV detection performance. In addition, when deployed on the RK3588-based embedded system, the method achieves a low false negative rate and exhibits robust detection capabilities, indicating strong potential for practical applications in airspace management and counter-UAV operations. Full article

This study proposes a behavioral monitoring framework for newborn piglets based on SPMF-YOLO object detection and ByteTrack multi-object tracking, which enables precise quantification of early postnatal activity levels and health assessment. The method enhances small-object detection performance by incorporating the SPDConv module, the MFM module, and the NWD loss function into YOLOv11. When combined with the ByteTrack algorithm, it achieves stable tracking and maintains trajectory continuity for multiple targets. An annotated dataset containing both detection and tracking labels was constructed using video data from 10 piglet pens for evaluation. Experimental results indicate that SPMF-YOLO achieved a recognition accuracy rate of 95.3% for newborn piglets. When integrated with ByteTrack, it achieves 79.1% HOTA, 92.2% MOTA, and 84.7% IDF1 in multi-object tracking tasks, thereby outperforming existing methods. Building upon this foundation, this study further quantified the cumulative movement distance of each newborn piglet within 30 min after birth and proposed a health-assessment method based on statistical thresholds. The results demonstrated an overall consistency rate of 98.2% across pens and an accuracy rate of 92.9% for identifying abnormal individuals. The results validated the effectiveness of this method for quantifying individual behavior and assessing health status in newborn piglets within complex farming environments, providing a feasible technical pathway and scientific basis for health management and early intervention in precision animal husbandry. Full article

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC. Full article