Search Results (209)

Accurately estimating the internal structure of deformable objects from sparse measurements remains a significant challenge in robotics. This work proposes a three-stage identification framework for this problem. First, a classification strategy determines a minimal informative set of indentation locations using a generalized error computed from pre-simulated FEM force reactions of baseline cavity models and flat-punch indentation estimation. Using this set, the estimation stage detects the cavity type and provides a preliminary estimate of its geometric parameters based solely on measured indentation responses. The correction stage then refines these parameters by replaying measured indentation depths in FEM simulations and deriving geometry corrections from the discrepancy between simulated and homogeneous force responses. Robust loss functions at both stages limit the influence of measurements where local contact conditions deviate from the assumed model, improving reliability across all tested cases. Indentation depth was obtained through gripper proprioception, with an RGB-D camera limited to global pose alignment. Experiments on soft cubes with spherical, cuboid, and pyramidal cavities demonstrate that, within known cavity families and fixed material parameters, the minimal indentation set reliably distinguishes cavity types and the pipeline reconstructs dimensions within error bounds. Extending the framework to non-centered structures and unknown materials remains future work. Full article

To improve the adaptability and adhesion of wall-climbing robots on complex curved surfaces, a self-adaptive spherical magnetic wheel robot is proposed for inspecting three-dimensional variable-curvature structures. The robot employs a bilateral wheeled design with passive magnetic modules that automatically adjust to contact conditions, ensuring efficient adhesion without active control. A Halbach-array magnetic circuit further enhances adhesion without increasing size or weight. Simulations analyze the effect of swing angle on adhesion and determine the minimum adaptable curvature radius. Experiments show stable climbing on surfaces with radii of 100–350 mm, obstacle-crossing up to 7 mm, and a payload capacity of 16.63 kg. Compared with existing designs, the robot offers improved curvature adaptability and load capacity under similar size and weight constraints. Full article

The exploration of lunar caves is a critical aspect of the space exploration program of the European Space Agency (ESA). To facilitate this mission, the DAEDALUS study investigated a novel spherical robot design in 2021. The proposed robot uses a unique telescopic linear rod mechanism to generate rotation and hence locomotion. This drive mechanism requires a dedicated control scheme to ensure both locomotion and simultaneously stabilization of the robot. The overall task of following a curved trajectory is also a problem that cannot be solved by simple algorithms. In this work, we introduce, calculate, and simulate a solution for these tasks, the Virtual Pose Instruction Plane (VPIP). The VPIP breaks the problem of multiple independent controllable rods down to two controllable parameters (roll and pitch of the plane), which control the linear motion velocity, balance and ultimately curvature motion of the robot. Initial simulations show that both speed and cornering can be controlled by the VPIP. Full article

When a rigid object is manipulated by dual arms to form a closed chain, the dual-arm motion must satisfy closed-chain constraints. Although synchronized motion can be achieved by strictly tracking predefined global trajectories, the presence of dynamic obstacles necessitates reactive local planning. However, existing local planning methods designed for single-arm manipulators cannot guarantee synchronization between dual arms. To address this limitation, we propose a dual-arm reactive synchronized motion controller (SMC) by incorporating closed-chain constraints on dual-arm slack velocities based on spherical geometric velocity constraints, and by implementing a flexible master-slave arm switching strategy. As a result, the proposed controller achieves synchronized dual-arm control while preserving excellent motion performance, including manipulability enhancement, obstacle avoidance, and compliance with joint angle and velocity constraints. Simulations and experiments on a humanoid upper-body robot validate the effectiveness of the proposed approach. Full article

Minimally invasive surgery (MIS) has transformed modern surgical operations by reducing pain, trauma, scarring and recovery time for the patient. However, precision, stability and accuracy continue to limit surgical performance. Robots can exhibit better precision and stability than humans and have the potential to improve MIS results. This work presents the design and development of a patented 3R1T parallel robot for MIS. The mechanism incorporates a coaxial spherical parallel architecture enabling three rotational degrees of freedom, combined with a remotely actuated translational fourth degree of freedom, therefore reducing the weight of the moving structure, decreasing inertial forces and increasing the system accuracy. The kinematic design is analyzed to achieve the required workspace, motor torque requirements are calculated, and a control system with integrated inverse kinematics is developed. A prototype was manufactured, and preliminary experiments were conducted to evaluate the orientation repeatability of the robot. Results demonstrated a repeatability of ±22.86 μm, commensurate with typical MIS constraints. This suggests that the proposed robot offers potential improvements in precision and control for minimally invasive surgical procedures, over traditional manual methods. Full article

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control. Full article

3D scene reconstruction serves as a fundamental technology with widespread applications in virtual reality, structural inspection, and robotic systems. While recent advances in 3D Gaussian Splatting have significantly enhanced scene reconstruction capabilities, the performance of such methods remains suboptimal when applied to highly reflective environments. To overcome this limitation, we introduce ARS-GS, a novel framework that integrates Anisotropic Spherical Gaussian reflection modeling and spherical harmonics diffuse approximation into a physically based rendering pipeline. This architecture incorporates a skip connection between the Anisotropic Spherical Gaussian module and the Gaussian primitives, effectively preserving surface details while maintaining computational efficiency. Comprehensive experimental evaluations validate the efficacy of ARS-GS across multiple datasets. Specifically, our method establishes new state-of-the-art quantitative benchmarks, achieving a peak signal-to-noise ratio of 38.30 and a structural similarity index measure of 0.997 on the neural radiance fields synthetic dataset, alongside a peak signal-to-noise ratio of 46.31 on the Gloss Blender dataset. Furthermore, on the challenging reflective neural radiance fields real-world dataset, our approach secures the highest peak signal-to-noise ratio scores, highlighted by a metric of 26.26 on the Sedan scene. The proposed method also substantially reduces perceptual errors, yielding a learned perceptual image patch similarity as low as 0.204, thereby consistently outperforming existing techniques in the reconstruction of highly specular surfaces with superior geometric fidelity. Full article

Unmanned spherical robots are autonomous mobile platforms with a fully enclosed spherical shell, providing high stability and strong adaptability to complex terrains. However, existing pendulum or flywheel spherical robots often suffer from limited maneuverability, whereas complex hybrid actuation schemes tend to compromise system stability. To address these issues, this study proposes an improved pendulum-driven spherical robot with a mechanically decoupled actuation design, integrating a pendulum system and a circular gear rack turning mechanism. This design enables smooth linear rolling as well as rapid in-place rotation, significantly enhancing maneuverability and motion flexibility on complex terrains. A dynamic model of the spherical robot is established to describe the decoupled actuation mechanism, and a fuzzy proportional–derivative (PD) control strategy is designed for rolling and steering control. Simulation and prototype experiments were conducted to evaluate trajectory tracking, steering response, and terrain adaptability. The results demonstrate that the proposed spherical robot achieves path following and in-place turning with robust mobility. Full article

As a core functional component of multi-degree-of-freedom precision motion mechanisms, spherical hinges are widely used in high-end equipment fields such as industrial robots, vehicle engineering, and intelligent manufacturing. Their dynamic performance directly determines the motion accuracy and the level of intelligent control of the equipment. The high-precision real-time measurement of two-degree-of-freedom (2-DOF) angles is a key prerequisite for achieving precise closed-loop control of spherical hinges. However, due to the strong coupling characteristics between the 2-DOF angle signals, it is difficult to directly and accurately measure the angular motion parameters of spherical hinges, which has become a core technical bottleneck restricting the improvement in their application efficiency. To address this challenge, this paper presents an improved study of the previously proposed spherical differential quadrature capacitance sensor for measuring the 2-DOF angle signals of spherical hinges. Firstly, the 2-DOF angle signal decoupling model is reconstructed and optimized. Secondly, a real-time decoupling circuit architecture for phase-shift detection with single-frequency signal excitation is innovatively proposed. This solution effectively addresses the incomplete decoupling of 2-DOF angle signals in previous studies, as well as the problems of considerable measurement noise, low resolution, and high calibration difficulty caused by random amplitude and phase errors in the excitation signals. Through the construction of an experimental platform for verification tests, the results show that the proposed scheme can significantly suppress the random errors caused by the parameter dispersion of the device, achieve an angle measurement resolution of 0.001°, and simultaneously considerably reduce the complexity of system calibration, laying a key technical foundation for the engineering application of spherical hinges in the fields of precision measurement and high-performance control. Full article

To evaluate cross-platform measurement consistency and diagnostic threshold requirements in shear wave elastography (SWE), this study presents a robotically controlled, phantom-based validation framework to quantify and interpret inter-vendor variability that limits clinical standardisation. A custom-fabricated polyvinyl chloride-graphite phantom containing eight spherical inclusions (15–25 mm diameter, 25–95 mm depth, 23.53–259.58 kPa stiffness), representing breast tissue mechanical properties, was evaluated using Samsung HS50 and Aixplorer ultrasound systems. Robotic automation standardised probe positioning and contact, eliminating operator-dependent variability and enabling direct, system-level comparison. Cross-platform reproducibility, accuracy against mechanically validated ground truth, and diagnostic threshold performance were assessed across 80 measurements. Both systems demonstrated excellent intra-machine reproducibility (coefficient of variation: Samsung 0.42%, Aixplorer 0.46%) with strong inter-machine correlation (r = 0.9951, p < 0.0001). However, systematic bias of 7.05 kPa and 95% limits of agreement spanning 38.7 kPa revealed substantial cross-platform measurement differences. All phantom inclusions (8/8) measured below their assigned ground truth stiffness on both systems, with systematic underestimation ranging from 0.33 kPa to 109.57 kPa, indicating parameter-dependent measurement distortion linked to inclusion size, depth, and stiffness. Dynamic range compression was observed (Samsung: 68.7%, Aixplorer: 59.1% of true phantom range), providing a mechanistic explanation for diagnostic threshold instability. This study contributes an interpretable validation methodology that links SWE measurement bias to physical lesion properties and imaging system characteristics, rather than relying on correlation alone. Despite strong reproducibility, the observed system-dependent bias demonstrates that SWE measurements are not directly transferable across ultrasound platforms, and system-specific collaboration is required to ensure reliable clinical interpretation. Full article

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces. Full article

Achieving reliable and real-time inverse kinematics (IK) for 6-degree-of-freedom (6-DoF) spherical-wrist manipulators remains a significant challenge. Analytical formulations often struggle with complex geometries and modeling errors, and standard numerical solvers (e.g., Levenberg–Marquardt) can stall near singularities or converge slowly. Purely data-driven approaches may require large networks and struggle with extrapolation. In this paper, we propose a low-latency, polynomial-based IK solution for spherical-wrist robots. The method leverages spherical coordinates and low-degree polynomial fits for the first three (positional) joints, coupled with a closed-form analytical solver for the final three (wrist) joints. An iterative partial-derivative refinement adjusts the polynomial-based angle estimates using spherical-coordinate errors, ensuring near-zero final error without requiring a full Jacobian matrix. The method systematically enumerates up to eight valid IK solutions per target pose. Our experiments against Levenberg–Marquardt, damped least-squares, and an fmincon baseline show an approximate 8.1× speedup over fmincon while retaining higher accuracy and multi-branch coverage. Future extensions include enhancing robustness through uncertainty propagation, adapting the approach to non-spherical wrists, and developing criteria-based automatic solution-branch selection. Full article

The development of upper-limb prostheses is often hindered by limited dexterity, a restricted workspace, and bulky designs, primarily due to performance limitations in proximal joints like the shoulder and elbow, which contribute to high user abandonment rates. To overcome these challenges, this paper presents a novel, bioinspired, and integrated prosthetic system as an advancement in bionic technology. The design incorporates a shoulder joint based on an asymmetric 3-RRR spherical parallel mechanism (SPM) with actuators embedded within the moving platform, and an elbow joint actuated by low-voltage Shape Memory Alloy (SMA) springs. The inverse kinematics of the shoulder mechanism was established, revealing the existence of up to eight configurations. We employed Multi-Objective Particle Swarm Optimization (MOPSO) to simultaneously maximize workspace coverage, enhance dexterity, and minimize joint torque. The optimized design achieves remarkable performance: (1) 85% coverage of the natural shoulder’s workspace; (2) a maximum von Mises stress of merely 3.4 MPa under a 40 N load, ensuring structural integrity; and (3) a sub-0.2 s response time for the SMA-driven elbow under low-voltage conditions (6 V) at a motion velocity of 6°/s. Both motion simulation and prototype testing validated smooth and anthropomorphic motion trajectories. This work provides a comprehensive framework for developing lightweight, high-performance prosthetic limbs, establishing a solid foundation for next-generation wearable robotics and bionic devices. Future research will focus on the integration of neural interfaces for intuitive control. Full article

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition design and an inverse mode auxiliary layering process, it ensures the uniform distribution of stress at different curvatures. The sensor adopts a 3 × 3 tactile array configuration, replicating the 3D curved surface distribution of human mechanoreceptors. By analyzing multi-point outputs, the sensor reconstructs contact pressure gradients and infers the softness or stiffness of touched objects, thereby realizing both structural and functional bionics. These sensors exhibit excellent linearity within 0–100 kPa (sensitivity ≈ 36.86 kPa⁻¹), fast response (2 ms), and outstanding durability (signal decay of only 1.94% after 30,000 cycles). It is worth noting that this conformal tactile fingertip integration method not only exhibits uniform responses at each unit, but also has the preload-free advantage, and then performs well in pulse detection and hardness discrimination. This work provides a novel bioinspired pathway for conformal integration of tactile sensors, enabling artificial skins and robotic fingertips with human-like tactile perception. Full article

This paper introduces a constraint-aware optimization framework for designing spherical multi-camera rigs that achieve complete panorama coverage while adhering to physical and field-of-view limitations. The approach assesses coverage using solid-angle geometry and calculates the sampling density in pixels per steradian, providing a measurable, traceable basis for panoramic optical measurement. By viewing panoramic imaging as a directional measurement challenge, the framework aligns with principles of optical metrology and guarantees uniform, non-contact optical sensing around the sphere. The optimization process includes capsule-based collision constraints, soft coverage losses, and field-of-view intersection modeling to produce physically feasible rig configurations. Experiments show that the optimized rigs provide improved coverage uniformity and less redundancy, with validation through Blender-generated synthetic panoramas confirming the practical performance of the designed optical systems. The proposed approach allows for systematic, measurement-driven design of spherical camera rigs for use in immersive imaging, robotic perception, and structural inspection. Full article