Search Results (40)

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, we introduce two key innovations: (1) an intent-aware human force generator capable of simulating aggressive, hesitant, oscillatory, conflicting, and nominal behaviors, through the modulation of force gains and the introduction of stochastic noise, and (2) the extension of VAC to incorporate variable stiffness as an adaptive control parameter alongside damping and inertia. The adaptive parameters are jointly tuned online using a self-supervised learning (SSL) mechanism driven by motion error metrics and interaction dynamics. The framework is simulated in a dual-arm collaborative manipulation scenario involving two 7-DoF Franka Emika Panda robots transporting a shared object in a high-fidelity simulation environment. Simulation results demonstrate the system’s capability to maintain stable behavior and minimize tracking error despite abrupt changes in human intent. This work provides a novel and systematic tool for stress-testing adaptive controllers in HRC, with implications for the design of resilient, safe, and reliable robotic systems in real-world collaborative environments. Full article

Walking on compliant terrain presents a substantial challenge for individuals with lower-limb amputation, further elevating their already high risk of falling. While powered ankle–foot prostheses have demonstrated adaptability across speeds and rigid terrains, control strategies optimized for soft or compliant surfaces remain underexplored. This work experimentally validates an admittance-based control strategy that dynamically adjusts the quasi-stiffness of powered prostheses to enhance gait stability on compliant ground. Human subject experiments were conducted with three healthy individuals walking on two bilaterally compliant surfaces with ground stiffness values of 63 and $25{\displaystyle \frac{\mathrm{kN}}{\mathrm{m}}}$, representative of real-world soft environments. Controller performance was quantified using phase portraits and two walking stability metrics, offering a direct assessment of fall risk. Compared to a standard phase-variable controller developed for rigid terrain, the proposed admittance controller reduced short-term maximum Lyapunov exponents by an average of 7%, indicating improved local dynamic stability. These results support the potential of adaptive prostheses control to enhance gait stability on compliant surfaces, contributing to the development of more robust human–prosthesis interaction. Full article

Understanding short-term glacier motion is vital for assessing ice sheet dynamics in a warming climate. This study investigates the tidal and diurnal influences on the flow of Helheim Glacier, one of Greenland’s fastest-flowing marine-terminating glaciers, using data from 18 high-frequency GPS sensors and a regional tide gauge collected during summer 2013. A Kalman filter was applied to separate and quantify glacier velocity, tidal admittance, and diurnal melt-driven acceleration. Results reveal a high level of tidal admittance affecting the horizontal flow speed of the glacier, especially at the centre of the glacier, which is propagated upstream. This admittance corresponds to a 0.38–0.68 m/day reduction from the mean at high spring tide and a comparable increase at low tide. The glacier’s vertical motion showed strong tidal control close to the terminus, of 0.6–1.05 m during high spring tides, but this was significantly reduced more than 1 km from the terminus. Diurnal variations in horizontal speed are less spatially and temporally variable, with most nodes experiencing changes from a mean speed of ±0.1–0.3 m/day. These findings demonstrate that both tidal forcing and meltwater input to the basal system exert a significant, and potentially spatially variable, control on glacier dynamics, highlighting the need to incorporate short-period external forcing into predictive models of marine-terminating glacier behaviour. Full article

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. Full article

In recent years, agricultural production activities have been advancing towards mechanization and intelligence to bridge the growing gap between the high labor intensity and time sensitivity of harvesting operations and the limited labor resources. As the component that directly interacts with target crops, the end-effector is a crucial part of agricultural harvesting robots. This paper first reviews their materials, number of fingers, actuation methods, and detachment techniques. Analysis reveals that three-fingered end-effectors, known for their stability and ease of control, are the most prevalent. Soft materials have gained significant attention due to their flexibility and low-damage characteristics, while the emergence of variable stiffness technology holds promise for addressing their issues of poor stability and fragility. The introduction of bionics and composite concepts offers potential for enhancing the performance of end-effectors. Subsequently, starting from an analysis of the biomechanical properties of fruits and vegetables, the relationship between mechanical damage and the intrinsic parameters of produce is elucidated. On the other hand, practical and efficient finite element analysis has been applied to various stages of end-effector research, such as structural design and grasping force estimation. Given the importance of compliance control, this paper explores the current research status of various control methods. It emphasizes that while hybrid force–position control often suffers from frequent controller switching, which directly affects real-time performance, active admittance control and impedance control directly convert external forces or torques into the robot’s reference position and velocity, resulting in more stable and flexible external control. To enable a unified comparison of end-effector performance, this review proposes a progressive comparison framework centered on control philosophy, comprising the ontological characteristic layer, physical interaction layer, feedback optimization layer, and task layer. Additionally, in response to the current lack of scientific rigor and systematization in performance evaluation systems for end-effectors, performance evaluation criteria (harvest success rate, harvest time, and damage rate) are defined to standardize the characterization of end-effector performance. Finally, this paper summarizes the challenges faced in the development of end-effectors and analyzes their causes. It highlights how emerging technologies, such as digital twin technology, can improve the control accuracy and flexibility of end-effectors. Full article

The effective use of nursing assistant robots requires an understanding of key acceptance factors. The study examined the differences in attitudes among 58 nursing students while performing ambulation tasks with and without an Adaptive Robotic Nursing Assistant (ARNA) robot. An ARNA is driven by tactile cues from the patient through a force–torque-measuring handlebar, whose signals are fed into a neuro-adaptive controller to achieve a specific admittance behavior regardless of patient strength, weight, or floor incline. Ambulation tasks used two fall-prevention devices: a gait belt and a full-body harness. The attitude toward the robot included perceived satisfaction, usefulness, and assistance, replacing the perceived ease-of-use construct found in the standard technology acceptance model. The effects of external demographic variables on those constructs were also analyzed. The modified technology acceptance model was validated with the simultaneous estimation of the effects of perceived usefulness and assistance on satisfaction. Our analysis employed an integrated hierarchical linear mixed-effects regression model to analyze the complex relationships between model variables. Our results suggest that nursing students rated the ARNA’s performance higher across all model constructs compared to a human assistant. Furthermore, male subjects rated the perceived usefulness of the robot higher than female subjects. Full article

The paper presents a shared control strategy that allows a human operator to physically guide the end-effector of a robotic manipulator to perform different tasks, possibly in interaction with the environment. To switch among different operational modes referring to a finite state machine algorithm, ElectroMyoGraphic (EMG) signals from the user’s arm are used to detect muscular contractions and to interact with a variable admittance control strategy. Specifically, a Support Vector Machine (SVM) classifier processes the raw EMG data to identify three classes of contractions that trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco² manipulator, equipped with force/torque sensor at the end-effector, and with a limited group of users wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb, demonstrating promising results. Full article

Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article

With the development of robotics, robots are playing an increasingly critical role in complex tasks such as flexible manufacturing, physical human–robot interaction, and intelligent assembly. These tasks place higher demands on the force control performance of robots, particularly in scenarios where the environment is unknown, making constant force control challenging. This study first analyzes the robot and its interaction model with the environment, highlighting the limitations of traditional force control methods in addressing unknown environmental stiffness. Based on this analysis, a variable admittance control strategy is proposed using the deep deterministic policy gradient algorithm, enabling the online tuning of admittance parameters through reinforcement learning. Furthermore, this strategy is integrated with a quaternion-based nonlinear model predictive control scheme, ensuring coordination between pose tracking and constant-force control and enhancing overall control performances. The experimental results demonstrate that the proposed method improves constant force control accuracy and task execution stability, validating the feasibility of the proposed approach. Full article

This research study proposes a heuristic hybrid deep neural network (DNN) gait sub-phase recognition model based on multi-source heterogeneous motion data fusion which quantifies gait phases and is applied in balance disorder rehabilitation control, achieving a recognition accuracy exceeding 99%. Building upon this model, a motion control strategy for a novel rehabilitation training robot is designed and developed. For patients with some degree of independent movement, an active training strategy is introduced; it combines gait recognition with a variable admittance control strategy. This strategy provides assistance during the stance phase and moderate support during the swing phase, effectively enhancing the patient’s autonomous movement capabilities and increasing engagement in the rehabilitation process. The gait phase recognition system not only provides rehabilitation practitioners with a comprehensive tool for patient assessment but also serves as a theoretical foundation for collaborative control in rehabilitation robots. Through the innovative active–passive training control strategy and its application in the novel rehabilitation robot, this research study overcomes the limitations of traditional rehabilitation robots, which typically operate in a single functional mode, thereby expanding their functional boundaries and enabling more precise, personalized rehabilitation training programs tailored to the needs of patients in different stages of recovery. Full article

To deal with the astronaut’s motion intention, as well as uncertainties in robotic dynamics, a human–spacesuit interaction (HSI) model is presented for the development of a joint-assisted exoskeleton robot in an active spacesuit using adaptive admittance control. Firstly, an adaptive RBF neural network control was designed for different astronauts, or the same astronauts in different states, which could be used to approximate the variable HSI model as a whole. Secondly, based on robust fuzzy control, the position inner loop of adaptive admittance control was designed to enhance the tracking effect for a given reference trajectory. When there is an interaction force between the active spacesuit and the wearer, the actual HSI force measured by the sensor transforms into the correction of the desired trajectory input, and the position inner loop tracks the corrected reference trajectory. The online estimation of stiffness is employed to assess the variable impedance property of a joint-assisted exoskeleton robot in an active spacesuit. Oxygen consumption decreased by 15.88% at most, which indicates that the proposed control method enables the wearer to effectively execute a simulated lunar sample collection mission with the joint-assisted exoskeleton robot. Full article

Traditional Chinese Bone-setting (TCB) involves complex movements and force feedback, which are critical for effective fracture reduction. However, its practice necessitates the collaboration of highly experienced surgeons, and the availability of expert resources is significantly limited. These challenges have significantly hindered the inheritance and dissemination of TCB techniques. The advancement of Learning from Demonstration offers a promising solution for addressing this challenge. In this study, we developed an innovative framework of closed reduction skill learning and dual-layer hybrid admittance control for a dual-arm bone-setting robot, specifically targeting ankle fracture. The framework began with a comprehensive structural design of the robot, incorporating analyses of closed-chain kinematics and the decomposition of internal and external forces. Additionally, we introduced a globally optimal reparameterization algorithm for temporal alignment of demonstrations and extended the Motion/Force Synchronous Kernelized Movement Primitive to learn reduction maneuvers and forces. Furthermore, we designed a dual-layer hybrid admittance controller, consisting of an ankle-layer and a robot- layer. Specifically, we propose a novel adaptive fuzzy variable admittance control strategy for the ankle-layer to achieve accurate tracking of reduction forces, which reduces the RMSE of force tracking along the X-axis by 50.35% compared to the non-fuzzy strategy. The experimental results demonstrated that the framework successfully replicates the human-like bone-setting process and can imitate personalized bone-setting trajectories under expert guidance. Full article

Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according to the force tracking error. However, the low-level motion tracking error is generally ignored completely. Recognizing this limitation, this paper first formulates the low-level motion tracking error as an unknown input disturbance, based on which a dynamic interaction model capturing both structured and unstructured uncertainties is developed. With the developed interaction model, an observer-based adaptive robust force controller is proposed to achieve accurate and robust force modulation for a robotic manipulator. Alongside the theoretical stability analysis, comparative experiments with the classical admittance control (AC), the adaptive variable impedance control (AVIC), and the adaptive force tracking admittance control based on disturbance observer (AFTAC) are conducted on a robotic manipulator across four scenarios. The experimental results demonstrate the significant advantages of the proposed approach over existing methods in terms of accuracy and robustness in interaction force control. For instance, the proposed method reduces the root mean square error (RMSE) by $91.3\%$, $87.2\%$, and $75.5\%$ in comparison to AC, AVIC, and AFTAC, respectively, in the experimental scenario where the manipulator is directed to follow a time-varying force while experiencing significant low-level motion tracking errors. Full article

To address the complex challenge of identifying the contact state between a shaft and a hole and to improve the efficiency of robotic shaft-hole assembly tasks, a robotic shaft-hole assembly method based on variable admittance control is proposed. In this method, admittance control serves as the foundational force controller for shaft-hole assembly. On this basis, the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm from deep reinforcement learning is utilized to optimize the parameters of the admittance controller. Additionally, a nonlinear reward function is designed, which not only prevents the assembly strategy from converging to local optima but also further accelerates the training speed of the assembly task. Experiments conducted with a collaborative robotic arm performing 15° inclined hole assembly demonstrated that the assembly efficiency of the variable admittance method was 9.6% higher than that of the fixed admittance parameter method, validating the feasibility and effectiveness of the proposed variable admittance shaft-hole assembly method. Full article