Search Results (2,214)

The application of robotic milling manipulators demonstrates a promising method for the efficient manufacturing of large-scale structures. However, the cutting accuracy and efficiency of milling robots are predominantly subjected to their low stiffness, which may easily cause chatter during machining. Accurate prediction of chatter stability for robots is of practical importance and is challenging. This paper develops a dynamic model of flexible link elements by considering link flexibility and joint torsional deformation and then constructs a multi-link flexible coupled dynamic model using the receptance coupling substructure analysis (RCSA) method. Subsequently, the equivalent dynamic parameters are identified via the particle swarm optimization (PSO) algorithm. On this basis, the end-effector frequency response functions (FRFs) of the robot under different poses are predicted, and the stability lobe diagram (SLD) for milling is generated based on chatter theory. Finally, the predicted FRFs and stability regions are validated through modal tests and milling experiments. Experimental results demonstrate that the proposed model can predict the end-effector dynamic characteristics and chatter occurrence conditions under different poses, confirming its effectiveness in the analysis of milling chatter stability. Quantitative validation yields a maximum error of 3% for predicted first-order modal frequencies and relative modal amplitude errors below 10%, with experimentally confirmed critical depths of cut of 0.1–0.2 mm at 3000 rev/min and 0.5–0.6 mm at 5000 rev/min. Full article

Continuum robots offer inherent compliance and dexterity for operation in confined and unstructured environments; however, achieving hybrid multi-segment functionality typically requires application-specific redesign and tightly coupled architectures. To address this limitation, this study proposes a reconfigurable hybrid continuum robot architecture based around a multi-lumen central integration backbone that supports multiple actuation modalities and robot configurations. The proposed design combines external tendon-driven disk modules for proximal actuation with a pneumatically actuated distal tip, while internal lumens allow routing of pneumatic lines and the insertion of optional stiffening elements without structural interference. The reconfigurability of the architecture is demonstrated through two configurations: Concept-1, a two-segment hybrid system, and Concept-2, a miniaturized three-segment configuration achieved by reducing the disk diameter and extending tendon actuation to the backbone. Experimental evaluations are conducted to characterize segment-wise actuation, coupled deformation behavior, and workspace capabilities, hysteresis response, tip contact force, and phantom-based target reachability. Results show that the integration of tendon-driven and pneumatic actuation significantly expands and reorients the reachable workspace. Additional functional tests showed repeatable loading–unloading behaviour of the tendon-driven segment, a maximum pneumatic tip contact force of approximately 0.45 N, and successful access to five representative targets within a stomach-like phantom using Concept-2. A kinematic model based on a constant-curvature formulation is validated against experimental data, yielding root-mean-square errors (RMSE) of 5.44 mm and 6.12 mm for Concept-1 and Concept-2, respectively. These results demonstrate consistent model accuracy across different configurations and scales. Overall, the proposed architecture enables modular, scalable, and reconfigurable hybrid continuum robots, providing a flexible framework for applications ranging from large-scale manipulation to gastroscopy-inspired minimally invasive procedures. Full article

Robust manipulation of small, densely stacked objects remains a challenging problem due to severe occlusions and geometric ambiguities, particularly under single-view sensing conditions. When observed using a single RGB-D sensor, adjacent surfaces of featureless cuboid objects, such as Jenga blocks, often merge in depth measurements, making reliable instance separation and pose estimation difficult. This paper presents a strategy-driven perception and manipulation framework for the robotic rearrangement of randomly stacked Jenga blocks under single RGB-D sensor constraints. The proposed approach employs a heightmap-based perception pipeline that integrates color filtering with geometric reasoning to segment individual blocks and estimate manipulation-compatible poses. Beyond perception, the proposed system determines robot actions through a structured manipulation policy consisting of region-wise search for directly executable grasps, grasp candidate evaluation based on accessibility and collision risk, selective local regrasping for workspace reconfiguration, and placement mode selection between direct insertion and sliding-assisted placement. In this framework, controlled grasp-and-release actions are applied only when no directly executable candidate is found within the currently scanned region and a suitable recovery target can be identified, thereby transforming cluttered local arrangements into more executable states without requiring additional sensing modalities. Experimental results, conducted under competition-equivalent conditions, demonstrate a high task success rate of 99.02%, confirming the robustness and reliability of the proposed framework. The results show that strategy-driven manipulation can effectively compensate for perception limitations in single RGB-D sensor environments, enabling stable and efficient pick-and-place operations in dense clutter. Full article

This study investigates the station planning problem of a mobile manipulator for robotic 3D concrete printing. The problem is formulated as a station planning problem considering two trajectory-level performance objectives: kinematic dexterity and structural stiffness. A directional dexterity metric based on the minimum normalized velocity directional manipulability along the task path is used to evaluate the worst-case motion capability of the manipulator during trajectory execution. A stiffness-related metric based on the maximum absolute Z-axis deformation of the end-effector is used to evaluate the worst-case deformation under operational loads. These two trajectory-level criteria are normalized and integrated through a weighted scalarization strategy, and a genetic algorithm is employed to search for station configurations under reachability constraints. Case studies on representative wall geometries show that the proposed method improves motion performance and reduces deformation compared with non-optimized station placements. The results indicate that the proposed framework provides an effective station planning strategy for mobile manipulators in trajectory-following robotic tasks. Full article

The dual-arm cooperative operation mode can effectively address the problems of insufficient load capacity and limited motion flexibility of traditional single-arm robots during the installation of construction boards. However, the selection of the end-effector grasping position of dual-arm robots will significantly affect their motion performance during handling operations. To address this issue, this study proposes an end-effector grasping strategy for sheet installation in the dual-arm cooperative operation mode of a dual-arm robot, which determines the optimal grasping position to ensure the robot’s good operational performance. We developed a dual-arm robot prototype for board installation and established a kinematic model of the robot’s manipulators. Based on the dexterity index’s service sphere, we obtained the dexterity envelope surfaces of the robot end-effector at different grasping distances and analyzed the relationship between grasping distance and dexterity. The mechanical model of the robot was established, and simulations were performed for each joint. The effects of different grasping points on the torque, stiffness, and stability at the robot’s key points were investigated, and the end-effector grasping range of the robot with optimal mechanical performance was analyzed. Finally, the proposed robot grasping strategy was verified on the robot prototype. The results demonstrate that the strategy is feasible and effective, helping to improve the robot’s operational performance. Full article

This study presents an integrated framework for enhancing the safety and operational efficiency of robotic arms in laparoscopic surgery by addressing minimum distance estimation between multi-arm manipulators and the associated collision-aware warning. By combining analytical modeling, real time simulation, and machine learning, the framework offers a robust solution for ensuring safe robotic operations. An analytical model was developed to estimate the minimum distances between robotic arms based on their joint configurations, offering theoretical calculations that serve as both a validation tool and a benchmark. To complement this, a 3D simulation environment was created to model two 7 DOF Kinova robotic arms (Kinova Inc., Boisbriand, QC, Canada), generating a diverse dataset of configurations for distance estimation and collision warning. Using these insights, a deep residual neural network model was trained with joint configurations as inputs. On the held out validation set, the model achieves $R^2=0.940$, RMSE $=42.0$ mm, MAE $=28.7$ mm, and a near zero mean bias, demonstrating strong predictive accuracy and consistent generalization across the workspace. The framework is intended as an early collision warning layer, where a warning is triggered when the predicted inter-arm distance falls below a 0.2 m threshold, which corresponds to a surface to surface clearance of approximately 50 mm given the Kinova Gen3 (Kinova Inc., Boisbriand, QC, Canada) cross sectional radius. This work demonstrates the effectiveness of combining analytical modeling with machine learning to enhance the precision and reliability of multi-arm robotic systems. Full article

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines. Full article

Unmanned aerial vehicles (UAVs) are widely used for monitoring and payload transport; however, their application in autonomous physical interaction remains limited due to payload constraints, stability challenges, and the complexity of integrating manipulation systems. This study presents the design and development of a lightweight foldable robotic arm based on the ten-bar Kempe Kite Inversor II linkage for UAV aerial manipulation. The mechanism generates precise straight-line motion using a single degree of freedom. Kinematic modeling and simulation validated a maximum end-effector reach of approximately 0.42 m. Structural optimization using additive manufacturing and honeycomb cellular architectures significantly reduced system weight while maintaining mechanical reliability. A passive compliant gripper, counterbalance mechanism, onboard storage net, and landing gear were integrated to evaluate the arm in a practical harvesting scenario using cherries as the test object. The final integrated system weighs 0.351 kg during operation, remaining approximately 16% below the experimentally determined UAV payload limit of 0.4185 kg. Proof-of-concept flight demonstrations confirmed successful aerial grasping of cherries, validating the feasibility of the proposed lightweight manipulation approach for agricultural applications. Full article

This review provides a comprehensive synthesis of robotic fruit harvesting systems, with a particular focus on the system-level integration of perception, manipulation, and fruit detachment within autonomous harvesting environments. Recent advances in machine vision, deep learning, sensor fusion, robotic end-effectors, grasping strategies, and motion planning are critically analyzed alongside cutting, pulling, and vibration-based detachment mechanisms under unstructured orchard conditions. Beyond component-level analysis, this review emphasizes the critical role of perception–action coupling and highlights key system integration challenges, including localization errors, perception-to-action latency, and environmental variability, which continue to limit reliable field deployment. In addition, orchard and pre-harvest-related factors such as canopy structure, fruit distribution, and detachment force variability are examined in relation to their direct impact on system performance, robustness, and harvesting efficiency. Furthermore, the review extends toward system-level considerations by incorporating performance evaluation metrics, economic feasibility, and scalability constraints, which are essential for transitioning robotic harvesting systems from experimental prototypes to commercially viable solutions, including practical field deployment in distributed and multi-robot harvesting systems. Emerging technologies, including artificial intelligence, advanced sensing, digital agriculture, and energy-aware system design, are discussed as key enablers for achieving adaptive, data-driven, and scalable autonomous harvesting. The novelty of this work lies in proposing an integrated framework that explicitly links perception, manipulation, and detachment with orchard-level constraints and deployment requirements, thereby bridging the gap between algorithmic advancements and real-world implementation of autonomous fruit harvesting systems. Full article

Magnetically controlled continuum robots (MCRs) emerge as a novel type of flexible robotic system that overcomes the physical limitations of traditional rigid-link structures, exhibiting high compliance, minimal invasiveness, and high spatial freedom. Through non-invasive, precise manipulation using magnetic fields, MCRs can achieve navigation and positioning in complex and confined microenvironments such as blood vessels and cavities in the human body. Furthermore, MCRs have attracted increasing attention for minimally invasive intervention because they combine structural compliance with remote magnetic actuation. In this study, we first introduce the driving control of MCRs, including the driving principle and driving system. Next, we discuss different types of robots, such as guiding and steering robots, variable stiffness robots, multimodal motion robots, and bio-inspired continuum robots, as well as their fabrication materials and manufacturing processes. Subsequently, we analyze the achievements of these robots in the medical field, including cardiovascular treatment, cavity diagnosis and treatment, and bone and joint treatment. The review also discusses current challenges in control accuracy, biocompatibility, system integration, and clinical translation. Finally, we briefly summarize the research and discuss the current challenges and future development directions of MCRs. Full article

Category-level generalization for robotic garment manipulation, such as bimanual smoothing, remains a significant hurdle due to high dimensionality, complex dynamics, and intra-category variations. Current approaches often struggle, either overfitting with concurrently learned visual features for a specific instance or, despite Category-level perceptual generalization, failing to predict the value of synergistic bimanual actions. We propose the Feature-Conditioned Bimanual Value Network (FCBV-Net), operating on 3D point clouds to specifically enhance intra-category policy generalization—generalizing across unseen variations within a single topological class, as distinct from cross-category transfer—for garment smoothing. FCBV-Net conditions bimanual action value prediction on pre-trained, frozen dense geometric features, ensuring robustness to intra-category garment variations. Trainable downstream components then learn a task-specific policy using these static features. In simulated PyFlex environments using the CLOTH3D dataset, FCBV-Net demonstrated superior intra-category generalization. It exhibited only an 11.5% efficiency drop (Steps80) on unseen garments compared to 96.2% for a 2D image-based baseline, and achieved 89% final coverage, outperforming an 83% coverage from a 3D correspondence-based baseline that uses identical per-point geometric features but a fixed primitive. These results highlight that the decoupling of geometric understanding from bimanual action value learning enables better intra-category generalization. Full article

Agriculture is currently transitioning toward higher intelligence and facility-based production, where harvesting robots play a crucial role in enhancing efficiency and ensuring standardized output. Addressing the challenges of inaccurate picking pose estimation and limited reachability in greenhouse environments, this paper proposes a reachable grasping pose estimation method based on Particle Swarm Optimization (PSO). First, initial poses are calculated via instance segmentation and keypoint extraction. Subsequently, a fitness function is constructed based on inverse kinematics, and the PSO algorithm is employed to iteratively search for optimal reachable poses. To further tackle planning difficulties in confined spaces, a two-stage path planning method based on cost maps is introduced. A series of performance metrics were designed to validate the proposed pose estimation and path planning methods through simulation experiments. In real-world field tests, the system achieved a harvesting success rate of 85%, significantly outperforming existing methods. The results demonstrate that the proposed approach substantially enhances the operational feasibility and success rate of tomato harvesting robots. Full article

Recent Vision–Language–Action (VLA) models have rapidly emerged as general-purpose robotic policies that integrate language understanding, visual perception, and robot control. However, prior studies and surveys have primarily emphasized backbone architectures, action decoders, training recipes, and benchmark performance, whereas relatively limited systematic attention has been given to sensor modality selection, heterogeneous signal alignment and fusion, and their connection to action generation, all of which are critical to the performance and safety of real-world robotic manipulation. This survey addresses this gap by reinterpreting VLA within the framework of a sensor–fusion–action pipeline. This study first presents a systematic taxonomy of major sensor modalities, including RGB, depth, tactile sensing, force/torque, proprioception and inertial measurement unit, multi-spectral/thermal, and event-based vision, and compares them in terms of the physical information they provide, their characteristic failure modes, and their deployment constraints. This survey further reviews teleoperation-, human video-, and simulation-based data collection pipelines, together with representative dataset configurations, and analyzes the multi-modal design space from a sensor-centric perspective, including early and late fusion, cross-attention, token-level fusion, adapters, mixture of experts, and multi-rate action representations. In addition, this study identifies a strong bias in existing benchmarks toward RGB-centric inputs and single success-rate metrics and emphasizes the need for a multidimensional evaluation framework incorporating robustness, worst-case performance, safety, latency, and efficiency. By shifting the focus away from a model-centric narrative and explicitly accounting for real-world sensor complexity, this survey seeks to establish a sensor-centered foundation for the next generation of Physical AI. Full article

To address the rigid temporal constraints and high-precision trajectory tracking requirements in modern industrial automation (e.g., high-speed pick-and-place or collaborative assembly), this paper proposes a novel composite control strategy for robotic manipulators that integrates Actor–Critic reinforcement learning with predefined-time sliding mode control (PTC-RLC). Existing predefined-time control (PTC) schemes usually rely on excessively large switching gains when dealing with strong disturbances, which easily triggers severe chattering in the system’s actuators and degrades dynamic performance. To this end, a novel predefined-time sliding surface based on artificial delay feedback is designed, ensuring that the position tracking error can strictly converge within a user-explicitly set time $T_c$ regardless of the system’s initial states, thereby significantly enhancing temporal determinism. Meanwhile, a reinforcement learning agent based on the Actor–Critic architecture is constructed to approximate and dynamically compensate for the system’s lumped unknown dynamics and external disturbances online, minimizing the control law’s reliance on large robust gains. Based on Lyapunov stability theory, the semi-global uniform ultimate boundedness of the closed-loop system is strictly proved. Numerical simulation results demonstrate that under severe operating conditions with parameter mismatches and time-varying disturbances, the proposed control strategy not only achieves high-precision and singularity-free trajectory tracking within the predefined time, but also effectively suppresses high-frequency chattering phenomena compared to the traditional non-singular terminal sliding mode control (NTSMC), outputting a smoother control torque and demonstrating strong potential for practical engineering implementations. Full article