Search Results (412)

In this article, the problem of control of the kinematic model of an omnidirectional robot with time delay in the control input is tackled through an Active Disturbance Rejection Control (ADRC) with a disturbance predictor-based scheme, which consists in predicting the generalized forward disturbance input in order to cancel it and then using a feedforward linearization approach to control the system in trajectory tracking tasks. The novelties of the scheme are to demonstrate that using the proposed extended state disturbance estimation leads to a forward estimation following the Taylor series approximation, and, to avoid using additional pose predictions, a feedforward input as an exact linearization approach is used, in which the remaining dynamics can be lumped into the generalized disturbance input. Thus, the use of extended states in prediction improves the robustness of the predictor while increasing the prediction horizon for larger time delays. The stability of the proposal is demonstrated using the second method of Lyapunov, which shows the closed-loop estimation/tracking ultimate bound behavior. Additionally, numerical simulations and experimental tests validate the robustness of the approach in trajectory-tracking tasks. Full article

Autonomous experimental platforms increasingly rely on robust, vision-guided robotic manipulation to support reliable and repeatable laboratory operations. This work presents a modular motion-execution subsystem designed for integration into self-driving laboratory (SDL) workflows, focusing on the coupling of real-time visual perception with smooth and stable manipulator control. The framework enables autonomous detection, tracking, and interaction with textured objects through a hybrid scheme that couples advanced motion planning algorithms with real-time visual feedback. Kinematic analysis of the manipulator is performed using the screw theory formulations, which provide a rigorous foundation for deriving forward kinematics and the space Jacobian. These formulations are further employed to compute inverse kinematic solutions via the Damped Least Squares (DLS) method, ensuring stable and continuous joint trajectories even in the presence of redundancy and singularities. Motion trajectories toward target objects are generated using the RRT* algorithm, offering optimal path planning under dynamic constraints. Object pose estimation is achieved through a a vision workflow integrating feature-driven detection and homography-guided depth analysis, enabling adaptive tracking and dynamic grasping of textured objects. The manipulator’s performance is quantitatively evaluated using smoothness metrics, RMSE pose errors, and joint motion profiles, including velocity continuity, acceleration, jerk, and snap. Simulation results demonstrate that the proposed subsystem delivers stable, smooth, and reproducible motion execution, establishing a validated baseline for the manipulation layer of next-generation SDL architectures. Full article

In view of the problems of high weed regeneration rate in traditional mechanical weeding and environmental risk in chemical weeding, a synergetic strategy of “mechanical damage + wound spraying mechanism” was proposed, and an intelligent weeding device combining synchronous cutting and spraying was designed to enhance the efficacy of herbicides and reduce their use. Focusing on the physical characteristics of weeds and the cutting mechanism, the analysis of the weed-cutting system and the force characteristics of the cutting tool were conducted. Key factors affecting cutting quality were identified, and their respective value ranges were determined. A targeted spraying system was developed, featuring a conical nozzle, DC diaphragm pump, and electromagnetic control valve. The Delta parallel manipulator, equipped with both the cutting tool and nozzle, was designed, and a kinematic model was established for both its forward and inverse movements. Genetic algorithms were applied to optimize structural parameters, aiming to ensure effective coverage of typical weed distribution areas within the working space. A simulated environment measurement was built to verify the motion accuracy of the manipulator. Field experiments demonstrated that the equipment achieved an 81.5% wound weeding rate on malignant weeds in the seedling stage at an operating speed of 0.6 m/s, with a seedling injury rate below 5%. These results validate the high efficiency of the integrated mechanical cutting and targeted spraying system, offering a reliable technical solution for green and intelligent weed control in agriculture. This study fills the blank of only focusing on recognition accuracy or weeding rate under a single weeding method, but lacks a cooperative weeding operation. Full article

Additive manufacturing technologies for metals are developing rapidly. Among them, wire arc additive manufacturing (WAAM) has become widespread due to its accessibility. However, parts produced using WAAM require surface post-processing; therefore, hybrid technologies have emerged that combine additive and subtractive processes within a single compact manufacturing complex. Such systems make it possible to organize single-piece and small-batch production, including for the repair and restoration of equipment in remote areas. For this purpose, hybrid equipment must be lightweight, compact for transportation, provide sufficient workspace, and be capable of folding for transport. This paper proposes the concept of a multifunctional metal 3D printer based on hybrid technology, where WAAM is used for printing, and mechanical post-processing is applied to obtain finished parts. To ensure both rigidity and low mass, a 3-UPU parallel manipulator and a worktable with two rotational degrees of freedom are employed, enabling five-axis printing and machining. The printer housing is foldable for convenient transportation. The kinematics of the proposed 3D printer are investigated as an integrated system. Forward and inverse kinematics problems are solved, the velocities and accelerations of the moving platform center are calculated, singular configurations are analyzed, and the workspace of the printer is determined. Full article

This study presents a magnetically actuated soft robot inspired by the peristaltic locomotion of flatworms, designed to replicate the biological locomotion of worms to achieve robust maneuverability. Fabricated entirely from photocurable soft resin, the robot features a flexible elastomeric body and two webbed fins with embedded soft magnets. By applying a vertically oscillating magnetic field, the robot achieves forward crawling through the coordinated bending and lifting of fins, converting oscillating magnetic fields into continuous undulatory motion that mimics the gait of flatworms. The experimental results demonstrate that the system maintains consistent bidirectional velocities in the range of 4–7 mm/s on flat surfaces. Beyond linear locomotion, the robot demonstrates effective terrain adaptability, navigating complex topographies, including curved obstacles up to 16 times its body thickness, by autonomously adopting a high-lifting kinematic strategy to overcome gravitational resistance. Furthermore, load-carrying tests reveal that the robot can transport a 6 g payload without velocity degradation. These findings underscore the robot’s efficacy in overcoming mobility constraints, highlighting promising applications in fields requiring non-invasive intervention, such as biomedical capsule endoscopy and industrial pipeline inspection. Full article

In modern shipbuilding, the structural complexity of ship components and the constrained workspace make robotic grinding prone to collisions. To improve safety and stability, this paper proposes a collision-free posture optimization method for ship-component operations. First, forward and inverse kinematic models are established, and postures along the path are organized into a directed graph. Feasible postures are then identified under joint-limit and singularity constraints. Directed bounding boxes and the GJK collision detection algorithm are applied to construct a collision-free posture set. An improved A* algorithm is then introduced. It incorporates a multi-source heuristic based on joint-space geometric distance and a safety-distance penalty to compute an optimal posture sequence with minimal joint deviation. This design promotes smooth transitions between consecutive postures. Simulation results show that the proposed method avoids robot–workpiece interference in constrained environments and improves obstacle avoidance and motion smoothness. Compared with the standard A* algorithm, the proposed approach reduces search time by 15.8% and increases the minimum safety distance by nearly fivefold. Compared with a non-optimized posture sequence, cumulative joint variation is reduced by up to 92.5%. The joint amplitude range decreases by an average of 41.2%, and the standard deviation of joint fluctuations decreases by 37.8%. The proposed method provides a generalizable solution for robotic measurement, assembly, and machining in complex and confined environments. Full article

Navigation errors due to drifting in inertial systems using low-cost sensors are some of the main challenges for land vehicle navigation in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose an autonomous navigation strategy with a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU), referred to as the wheeled inertial navigation system (INS), to effectively suppress drifted navigation errors. The position, velocity, and attitude (PVA) of the vehicle are predicted through the inertial mechanization algorithm, while gyro outputs are utilized to derive the vehicle’s forward velocity, which is treated as an observation with non-holonomic constraints (NHCs) to estimate the inertial navigation error states. To establish a theoretical foundation for wheeled INS error characteristics, a comprehensive system observability analysis is conducted from an analytical point of view. The wheel rotation significantly improves the observability of gyro errors perpendicular to the rotation axis, which effectively suppresses azimuth errors, horizontal velocity, and position errors. This leads to the superior navigation performance of a wheeled INS over the traditional odometer (OD)/NHC/INS. Moreover, a hybrid extended particle filter (EPF), which fuses the extended Kalman filter (EKF) and PF, is proposed to update the vehicle’s navigation states. It has the advantages of (1) dealing with the system’s non-linearity and non-Gaussian noises, and (2) simultaneously achieving both a high level of accuracy in its estimation and tolerable computational complexity. Kinematic field test results indicate that the proposed wheeled INS is able to provide an accurate navigation solution in GNSS-denied environments. When a total distance of over 26 km is traveled, the maximum position drift rate is only 0.47% and the root mean square (RMS) of the heading error is 1.13°. Full article

In a fully autonomous vehicle, the driver becomes a passenger, free to adopt different seating positions. This change challenges traditional passive safety systems—such as seatbelts, airbags and seat design—that are optimised for a forward-facing position. As autonomous vehicles are integrated into mixed traffic with conventional cars, solutions need to address these challenges. In this intermediate stage, fully autonomous cars will need a system that, in the event of an accident, can rotate the seats to the most ideal position tested by the manufacturer. This could be a number of positions where the seat, airbags and seatbelts are optimised, taking into account the expected direction of impact. It is important that the rotation is not too radical, as this would increase the risk of injury. In addition, the seat dimensions need to be increased to improve energy absorption in the event of a collision, thereby reducing the impact forces on the occupants and improving overall safety. To improve passive protection, airbags will continue to be used in the future, but in completely new positions, sizes and shapes. This research aims to identify potential passive occupant safety solutions for seat positions that have been rotated in fully autonomous vehicles. The finite element simulation model on which the results in this article are based was developed in an earlier phase of the research. The current research combines two previously conducted research directions, using the modified seat and the developed airbag concept. This research’s main outcome is a system that effectively protects occupants in rotated seat positions. It maintains all evaluated injury criteria below their threshold limits and ensures controlled occupant kinematics. Full article

Soft pneumatic glove actuators for hand rehabilitation require compact, accurate models that can be evaluated in real time. At the same time, high-fidelity finite element (FE) simulations are too slow for iterative design and control. We develop a finite element-based calibration pipeline that combines a dependency-constrained human finger kinematic model with a segmented pseudo-rigid-body (PRB) description of ribbed-bellow soft pneumatic actuators sized to individual fingers. FE models with symmetry and contact generate pressure–pose data for the MCP, PIP, and DIP spans, from which we extract per-segment bending angles and axial elongations, fit simple pressure–kinematics relations, and identify PRB parameters using basin-hopping global optimization. The calibrated PRB reproduces FE flexion–extension trajectories for index and little finger actuators with millimetric accuracy (mean segment positioning errors of approximately 2.3 mm and 0.7 mm), preserves finger-like bending localized in the bellows, and maintains negligible compression of inter-joint links (below 1.2%). The pressure–bend and pressure–elongation maps achieve near-unity adjusted $R^2$, and the PRB forward kinematics evaluates complete pressure trajectories in less than half a millisecond, compared with several hours for the corresponding FE simulations. This pipeline provides a practical route from detailed FE models to controller-ready reduced-order surrogates for design-space exploration and patient-specific control of soft rehabilitation actuators. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic attention to body parts in imitation learning between humans and robots based on a Transformer model. To adapt human imitation movements to a robot, we solved forward and inverse kinematics using the Levenberg–Marquardt method and performed feature extraction using the k-means method to make the data suitable for Transformer input. The imitation learning process is carried out using the Transformer. UMAP is employed to visualize the attention layer within the Transformer. As a result, this system enabled imitation of movements while focusing on multiple body parts between humans and robots with an embodiment gap, revealing the transitions of body parts receiving attention and their relationships in the robot’s acquired imitation movements. Full article

Serial-parallel hybrid manipulators featuring remote actuation via parallelogram mechanisms are highly valued for their low inertia and high stiffness. However, the complex nonlinear errors introduced by their multi-stage transmission chains pose significant challenges for high-precision calibration. To address this, this paper proposes a hierarchical and decoupled calibration framework specifically tailored for such parallelogram-driven hybrid manipulators. The method first independently calibrates the pose error of the 3-DOF serial main arm using a composite error model that integrates transmission chain constraints. Subsequently, the 2-DOF parallel wrist is accurately calibrated employing a joint-space error identification strategy based on inverse kinematics, thereby circumventing the intractability of solving the parallel mechanism’s forward kinematics. Experimental validation was performed on a self-developed 5-DOF robot prototype using an optical tracker and an attitude sensor. Results from the validation dataset demonstrate that the proposed method reduces the robot’s average positioning error from 2.199 mm to 0.658 mm (a 70.1% improvement) and the average attitude error from 0.8976 deg to 0.1767 deg (an 80.3% improvement). Furthermore, comparative experiments against the standard MDH model and polynomial fitting models confirm that the proposed composite error model and multi-stage transmission error model are essential for achieving high accuracy. This research provides crucial theoretical insights and practical solutions for the high-precision application of complex remote-driven hybrid manipulators. Full article

To address longstanding challenges in sea buckthorn harvesting—such as the absence of effective harvesting principles, inefficient traditional manual and semi-mechanised methods, and rising labour costs—this study developed a self-propelled harvester equipped with a reciprocating cutter. The harvester featured an optimised double-support reciprocating cutter driven by a swing ring mechanism, with its kinematic parameters and cutting speed determined through analytical analysis. A coordinated transport system, consisting of an arc-shaped branch dial wheel, a conveying device, and a hydraulic system, was also designed. Field experiments were conducted employing a three-factor, three-level Box–Behnken design of Response Surface Methodology (RSM), which enabled the establishment of a predictive mathematical model for harvesting performance. Numerical optimisation via the model yielded the optimal operational parameters: harvesting forward speed of 0.6 m·s⁻¹, a cutting speed of 1.2 m·s⁻¹, and a conveyor belt linear speed of 0.8 m·s⁻¹. With this parameter combination, the missed cutting rate was 6.72%, fruit breakage rate 4.06%, and conveyor failure rate 7.79%, all meeting mechanised harvesting standards. This research provides the essential theoretical foundation and technical solutions for harvesting equipment in the sea buckthorn industry, accelerating its mechanisation process. Full article

Robotic Spatial Augmented Reality (RSAR) systems present a unique control challenge as their end-effector is a projection, whose final position depends on both the actuator’s pose and the external environment’s geometry. Accurately controlling this projection first requires predicting the 6-DOF pose of a projector-camera unit from joint angles; however, loose kinematic specifications in many RSAR setups make precise analytical models unavailable for this task. This study proposes a novel deep learning model combining Long Short-Term Memory (LSTM) and an Attention Mechanism (LSTM–Attention) to accurately estimate the forward kinematics of a 2-axis Pan-Tilt actuator. To ensure a fair evaluation of intrinsic model performance, a simulation framework using Unity and unified robot description format was developed to generate a noise-free benchmark dataset. The proposed model utilizes a multi-task learning architecture with a geodesic distance loss function to optimize 3-dimensional position and 4-dimensional quaternion rotation separately. Quantitative results show that the proposed LSTM–Attention model achieved the lowest errors (Position MAE: 18.00 mm; Rotation MAE: 3.723 deg), consistently outperforming baseline models like Random Forest by 9.5% and 17.6%, respectively. Qualitative analysis further confirmed its superior stability and outlier suppression. The proposed LSTM–Attention architecture proves to be a effective and accurate methodology for modeling the complex non-linear kinematics of RSAR systems. Full article

Although parallel mechanisms are used in various fields, their application is often limited by singularities and a restricted workspace. Kinematic redundancy is a promising approach for mitigating these issues while also extending the functionality of the mechanisms. This article contributes to this field by introducing two novel Delta-type kinematically redundant parallel mechanisms with linear actuators. The moving platform in these mechanisms has three translational degrees of freedom and consists of two parts connected by a prismatic joint, providing an extra translation between the parts. First, we present closed-form solutions to the inverse and forward kinematic problems, accompanied by numerical examples that validate the theoretical derivations. Next, we analyze singular configurations of the mechanisms with a symmetrical design, focusing on parallel singularities. Using an iterative approach, we identify points within the workspace corresponding to these configurations, including finite-motion singularities. Based on this analysis, we changed the geometrical parameters of one mechanism and presented the design where the singularity-free region of the workspace occupies 95% of the total workspace. This study forms the basis for future research on the proposed mechanisms and their prototyping. Full article