Search Results (594)

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

To resolve the inherent coupling conflict between trajectory tracking and lateral stability in distributed electric drive buses, this paper proposes a hierarchical cooperative control framework. A simplified two-degree-of-freedom (2-DOF) vehicle model is first established, and kinematically derived reference states for stable motion are computed. At the upper level, a model predictive controller (MPC) generates real-time steering commands while explicitly minimizing lateral tracking error. At the lower level, a proportional integral derivative (PID)-based roll moment controller and a linear quadratic regulator (LQR)-based direct yaw moment controller are designed, with four-wheel torque distribution achieved via quadratic programming subject to friction circle and vertical load constraints. Co-simulation results using TruckSim and MATLAB/Simulink demonstrate that, during high-speed single-lane-change maneuvers, peak lateral error is reduced by 11.59–18.09%, and root-mean-square (RMS) error by 8.67–14.77%. Under medium-speed double-lane-change conditions, corresponding reductions of 3.85–12.16% and 4.48–11.33% are achieved, respectively. These results fully validate the effectiveness of the proposed strategy. Compared with the existing MPC–direct yaw moment control (DYC) decoupled control framework, the coordinated control strategy proposed in this paper achieves the optimal trade-off between trajectory tracking and lateral stability while maintaining the quadratic programming solution delay below 0.5 milliseconds. Full article

This paper introduces a hypergraph-based formalism for modeling kinematic and dynamic structures in robotics, addressing limitations of the existing formats such as Unified Robot Description Format (URDF), MuJoCo-XML, and Simulation Description Format (SDF). Our method represents mechanical constraints and connections as hyperedges, enabling the native description of multi-joint closures, tendon-driven actuation, and multi-physics coupling. We present a tensor-based representation derived via star-expansion, implemented in the Hypergraph Model Cognition Framework (HyMeKo) language. Comparative experiments show a substantial reduction in model verbosity compared to URDF while retaining expressiveness for large-language model integration. The approach is demonstrated on simple robotic arms and a quarter vehicle model, with derived state-space equations. This work suggests that hypergraph-based models can provide a modular, compact, and semantically rich alternative for the next-generation simulation and design workflows. The introduced formalism reaches 50% reduction compared to URDF descriptions and 20% reduction compared to MuJoCo-XML descriptions. Full article

To address the issue of jitter and oscillation of guidance command during multi-vehicle cooperative engagement with maneuvering platforms, this paper proposes a novel terminal guidance law with prescribed performance constraints for multiple cooperative vehicles, which explicitly considers both transient and steady-state performance. Firstly, based on the vehicle-target relative kinematics, with time and space as the main constraint indicators, a multi-vehicle cooperative guidance model is established in the inertial coordinate system. Secondly, combined with the sliding mode control theory, cooperative guidance laws are designed for both the line-of-sight (LOS) direction and the LOS normal direction, respectively, and the Lyapunov stability proof is given. Furthermore, to counteract the impact of target maneuvers on guidance performance, a non-homogeneous disturbance observer is designed to estimate target maneuver information that is difficult to obtain directly, which ensures that performance constraints are still satisfied under strong target maneuvering conditions. Simulation results demonstrate that the proposed guidance law enables multiple coordinated vehicles to successfully engage the target under different maneuvering modes, while satisfying the terminal time-space constraints. Compared with conventional sliding mode control methods exhibiting inherent chattering, the proposed approach employs a novel PPC-SMC hybrid structure to quantitatively constrain the transient convergence of cooperative errors. This structure enhances the multi-vehicle cooperative guidance performance by effectively eliminating chattering and oscillations in the guidance commands, thereby significantly improving the system’s transient behavior. Full article

We propose a speed-adaptive robot accurate path-tracking framework based on the soft actor–critic (SAC) and Stanley methods (STANLY_ASAC). First, the Lidar–Inertial Odometry Simultaneous Localization and Mapping (LIO-SLAM) method is used to map the environment and the LIO-localization framework is adopted to achieve real-time positioning and output the robot pose at 100 Hz. Next, the Rapidly exploring Random Tree (RRT) algorithm is employed for global path planning. On this basis, we integrate an improved A* algorithm for local obstacle avoidance and apply a gradient descent smoothing algorithm to generate a reference path that satisfies the robot’s kinematic constraints. Secondly, a network classification model based on U-Net is used to classify common road surfaces and generate classification results that significantly compensate for tracking accuracy errors caused by incorrect road surface coefficients. Next, we leverage the powerful learning capability of adaptive SAC (ASAC) to adaptively adjust the vehicle’s acceleration and lateral deviation gain according to the road and vehicle states. Vehicle acceleration is used to generate the real-time tracking speed, and the lateral deviation gain is used to calculate the front wheel angle via the Stanley tracking algorithm. Finally, we deploy the algorithm on a mobile robot and test its path-tracking performance in different scenarios. The results show that the proposed path-tracking algorithm can accurately follow the generated path. Full article

This study investigates the 3RPUR (3-Revolute–Prismatic–Universal–Revolute) variable parallel mechanism, employing screw theory and linear geometry to analyze the geometric relationships and constraint characteristics of the RPUR (Revolute–Prismatic–Universal–Revolute) limb kinematic pairs. The findings reveal that the constraint moment in the always remains perpendicular to the two axes of the U pair, forming an equivalent plane. Through the locking/unlocking mechanism of universal joints (U pair), the mechanism achieves dynamic degree-of-freedom reconstruction, enabling seamless switching between three translational (3T) and three translational-one-rotation (3T1R) motion modes. The continuity between motion and degrees of freedom during the variable cell process is demonstrated. This research reveals a strict 1:1 linear coupling between the rotational angle of the moving platform around the Z-axis and the U pair’s rotation angle under 3T1R mode. Simulation experiments validate the feasibility and coupling characteristics of both motion modes, providing theoretical and technical support for this mechanism’s adaptation to complex working conditions in mobile robotics applications, particularly where reconfigurable parallel mechanisms are required for multi-task flexibility. Full article

Ducted fan flying robots with robotic arms can perform physical interaction tasks in complex environments such as indoors. However, the coupling effects between the aerial platform, the robotic arm, and physical environment pose significant challenges for the robot to accurately approach and stably contact the target. To address this problem, we propose a unified control framework for a ducted fan flying robot that encompasses both flight planning and physical interaction. This contribution mainly includes the following: (1) A nonlinear model predictive control (NMPC)-based trajectory optimization controller is proposed, which achieves accurate and smooth tracking of the robot’s end effector by considering the coupling of redundant states and various motion and performance constraints, while avoiding potential singularities and dangers. (2) On this basis, an easy-to-practice hierarchical control framework is proposed, achieving stable and compliant contact of the end effector without controller switching between the flight and interaction processes. The results of experimental tests show that the proposed method exhibits accurate position tracking of the end effector without overshoot, while the maximum fluctuation is reduced by up to 75.5% without wind and 71.0% with wind compared to the closed-loop inverse kinematics (CLIK) method, and it can also ensure continuous stable contact of the end effector with the vertical wall target. Full article

With the advancement of precision agriculture, the autonomous navigation of unmanned agricultural ground vehicles (UAGVs) has emerged as a critical research topic. As a fundamental component of autonomous navigation, path-tracking control is essential for ensuring the accurate and stable operation of UAGVs. However, achieving high-precision and robust tracking in agricultural environments remains challenging due to unstructured terrain, variable wheel slip, and complex dynamic disturbances. This review provides a structured and comprehensive survey of modeling and control methodologies for UAGVs, with particular emphasis on control-theoretic formulations and their applicability across diverse agricultural scenarios. In contrast to prior reviews, the modeling approaches are systematically classified into geometric, kinematic, and dynamic models, including extended formulations that incorporate wheel slip and external disturbances. Furthermore, this paper systematically reviews commonly adopted path-tracking strategies for UAGVs, including proportional–integral–derivative (PID) control, pure pursuit (PP), Stanley control, sliding mode control (SMC), model predictive control (MPC), and learning-based approaches. Emphasis is placed on their theoretical underpinnings, tracking accuracy, adaptability to unstructured field environments, and computational efficiency. In addition, several key technical challenges are identified, such as terrain-adaptive vehicle modeling, slip compensation mechanisms, real-time implementation under hardware constraints, and the cooperative control of multiple UAGVs operating in dynamic agricultural scenarios. By presenting a detailed review from a control-centric perspective, this study aims to serve as a valuable reference for researchers and practitioners developing intelligent agricultural vehicle systems. Full article

Considering the importance of maritime cybersecurity, this study provides a solution based on a nonlinear finite-time extended state observer (NFTESO) for unmanned surface vessels (USVs) equipped with rotatable thrusters under false data injection attacks (FDIAs). First, to complete the control design for USVs in a network environment and ensure optimal tracking performance within limits, an event triggering mechanism with finite-time constraints and a concise control optimization framework are employed. Then, command filtered technology is applied to obtain the derivative of the virtual control quantity generated using a backstepping design, optimizing the information interaction process in the kinematic and dynamic loops. The design based on the NFTESO estimates the composite uncertain dynamics in the system, including FDIAs, reducing the adverse effects of cyber attacks on the system. Finally, simulation outcomes confirmed the efficacy of the proposed control strategy. The simulation results showed that, compared with two other control schemes, the control scheme designed in this paper improved lateral tracking accuracy by approximately $77.1\%$ and $94.7\%$, and longitudinal tracking accuracy by approximately $95\%$ and $98\%$, respectively. Communication frequency was reduced by approximately $98.82\%$ and $82.48\%$, respectively. Full article

With the rapid development of deep-sea resource exploration and marine scientific research, wheeled remotely operated vehicles (ROVs) have become crucial for seabed operations. However, under complex seabed conditions, traditional ROV control systems suffer from insufficient trajectory tracking accuracy, poor disturbance rejection capability, and low dynamic torque distribution efficiency. These issues lead to poor motion stability and high energy consumption on sloped terrains and soft substrates, which limits the effectiveness of deep-sea engineering. To address this, we proposed a comprehensive motion control solution for deep-sea wheeled ROVs. To improve modeling accuracy, a coupled kinematic and dynamic model was developed, together with a body-to-terrain coordinate frame transformation. Based on rigid-body kinematics, three-degree-of-freedom kinematic equations incorporating the slip ratio and sideslip angle were derived. By integrating hydrodynamic effects, seabed reaction forces, the Janosi soil model, and the impact of subsidence depth, a dynamic model that reflects nonlinear wheel–seabed interactions was established. For optimizing disturbance rejection and trajectory tracking, a hierarchical control method was designed. At the kinematic level, an improved model predictive control framework with terminal constraints and quadratic programming was adopted. At the dynamic level, non-singular fast terminal sliding mode control combined with a fixed-time nonlinear observer enabled rapid disturbance estimation. Additionally, a dynamic torque distribution algorithm enhanced traction performance and trajectory tracking accuracy. Full article

This work presents the experimental validation of a reset control mode for a Quasi-Spherical Parallel Manipulator (qSPM), designed as a master device for bilaterally teleoperated telesurgical systems. The reset functionality enables autonomous repositioning of the master device to its central configuration via a joint-space path planning algorithm, executed entirely within the local control loop. Given the non-convex nature of the joint space, the algorithm computes feasible trajectories using a simplified optimization scheme that ensures compliance with mechanical and kinematic constraints. The algorithm was implemented within an ROS Noetic framework and tested across multiple scenarios, including both simulated and physical configurations. The experimental results confirm the device’s ability to reset to the central position in approximately 5 s, maintaining an average residual error below $0.25°$. Computational evaluations demonstrate that each path is generated in less than 10 milliseconds, supporting real-time execution. Additional trials show successful motion toward arbitrary points within the joint space, suggesting the potential for future integration of user-driven repositioning features. These findings highlight the responsiveness, reliability, and experimental feasibility of the proposed control mode, marking a key step toward improving usability in telesurgical environments. Full article

The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional algorithms due to the time-varying environment and the highly coupled multi-system dynamics of the airship. This study proposes a deep reinforcement learning algorithm, termed reward-prioritized Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (RPL-TD3). The method incorporates an LSTM network to effectively capture the influence of historical states on current decision-making, thereby improving performance in tasks with strong temporal dependencies. Furthermore, to address the slow convergence commonly seen in off-policy methods, a reward-prioritized experience replay mechanism is introduced. This mechanism stores and replays experiences in the form of sequential data chains, labels them with sequence-level rewards, and prioritizes high-value experiences during training to accelerate convergence. Comparative experiments with other algorithms indicate that, under the same computational resources, RPL-TD3 improves convergence speed by 62.5% compared to the baseline algorithm without the reward-prioritized experience replay mechanism. In both simulation and generalization experiments, the proposed method is capable of planning feasible paths under kinematic and energy constraints. Compared with peer algorithms, it achieves the shortest flight time while maintaining a relatively high level of average residual energy. Full article

This paper addresses the spatial path tracking problem of the X-rudder autonomous underwater vehicle (AUV) under random sea current disturbances. An adaptive line-of-sight guidance-linear quadratic regulator (ALOS-LQR) control strategy with roll constraints is proposed to enhance the tracking control accuracy and stability of the X-rudder AUV in such environments. First, to mitigate the roll-instability-induced depth and heading coupling deviations caused by unknown environmental disturbances, a roll-constrained linear quadratic regulator (LQR) heading-pitch control strategy is designed. Second, to handle random disturbances and model uncertainties, a nonlinear extended state observer (ESO) is employed to estimate dynamic disturbances. At the kinematic level, an adaptive line-of-sight guidance method (ALOS) is utilized to transform the path tracking problem into a heading and pitch tracking problem, while compensating in real time for kinematic deviations caused by time-varying sea currents. Finally, the effectiveness of the proposed control scheme is validated through simulation experiments and lake trials. The results confirm the effectiveness of the proposed method. Specifically, the roll-constrained ESO-LQR reduces lateral and longitudinal errors by 77.73% and 80.61%, respectively, compared to the roll-constrained LQR. ALOS navigation reduced lateral and longitudinal errors by 85.89% and 94.87%, respectively, compared to LOS control, while exhibiting faster convergence than ILOS. In physical experiences, roll control reduced roll angle by 50.52% and depth error by 33.3%. Results demonstrate that the proposed control strategy significantly improves the control accuracy and interference resistance of the X-rudder AUV, exhibiting excellent accuracy and stability. Full article

Robust control techniques are crucial for deploying robotic solutions in real applications and handling model uncertainties in robotic manipulators. The inertial parameters are fundamental to implementing control algorithms. While theoretical approaches to compute the system dynamics and the regressor matrix are well-established, they are computationally expensive and a practical implementation framework is still lacking. To address this challenge, we developed a new and efficient method to compute the Coriolis matrix based on Christoffel’s symbols. The result forms the basis of Thunder Dynamics, an open-source software package able to create standalone libraries that compute the system kinematics and dynamics for real-time adaptive control implementation. Thunder Dynamics enables users to create and compile user-defined functions on a robot, which can then be used in C++ or Python 3. To test the proposed framework, we implemented a Cartesian adaptive backstepping controller with axis-angle orientation using our tool. We tested the controller on a seven-degrees-of-freedom manipulator in both simulation and real-world scenarios, varying the levels of uncertainties in the inertial parameters. The results demonstrated that Thunder Dynamics is capable of meeting computational constraints given by the control loop frequency of real systems, permitting, for example, the implementation of advanced controls on commercial manipulators. Full article

To address the limitations of traditional mirror therapy in stroke rehabilitation, such as rigid movement mapping and insufficient personalization, this study proposes a robot-assisted mirror rehabilitation framework integrating multimodal biofeedback. By synchronously capturing kinematic features of the unaffected upper limb and surface electromyography (sEMG) signals from the affected limb, a dual-modal feature fusion network based on a cross-attention mechanism is developed. This network dynamically generates a time-varying mirror ratio coefficient $\lambda$, which is incorporated into the exoskeleton’s admittance control loop. Combining a trajectory generation algorithm based on dynamic movement primitives (DMPs) with a compliant control strategy incorporating dynamic constraints, the system achieves personalized rehabilitation trajectory planning and safe interaction. Experimental results demonstrate that, compared to traditional mirror therapy, the proposed system exhibits superior performance in bilateral trajectory covariance metrics, the mirror symmetry index, and muscle activation levels. Full article