Force/position tracking control of fracture reduction robot based on nonlinear disturbance observer and neural network.

BACKGROUND: For the fracture reduction robot, the position tracking accuracy and compliance are affected by dynamic loads from muscle stretching, uncertainties in robot dynamics models, and various internal and external disturbances. METHODS: A control method that integrates a Radial Basis Function Neural Network (RBFNN) with Nonlinear Disturbance Observer is proposed to enhance position tracking accuracy. Additionally, an admittance control is employed for force tracking to enhance the robot's compliance, thereby improving the safety. RESULTS: Experiments are conducted on a long bone fracture model with simulated muscle forces and the results demonstrate that the position tracking error is less than ±0.2 mm, the angular displacement error is less than ±0.3°, and the maximum force tracking error is 26.28 N. This result can meet surgery requirements. CONCLUSIONS: The control method shows promising outcomes in enhancing the safety and accuracy of long bone fracture reduction with robotic assistance.

Robot Learning Method for Human-like Arm Skills Based on the Hybrid Primitive Framework.

This paper addresses the issue of how to endow robots with motion skills, flexibility, and adaptability similar to human arms. It innovatively proposes a hybrid-primitive-frame-based robot skill learning algorithm and utilizes the policy improvement with a path integral algorithm to optimize the parameters of the hybrid primitive framework, enabling robots to possess skills similar to human arms. Firstly, the end of the robot is dynamically modeled using an admittance control model to give the robot flexibility. Secondly, the dynamic movement primitives are employed to model the robot's motion trajectory. Additionally, novel stiffness primitives and damping primitives are introduced to model the stiffness and damping parameters in the impedance model. The combination of the dynamic movement primitives, stiffness primitives, and damping primitives is called the hybrid primitive framework. Simulated experiments are designed to validate the effectiveness of the hybrid-primitive-frame-based robot skill learning algorithm, including point-to-point motion under external force disturbance and trajectory tracking under variable stiffness conditions.

Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking.

In the field of aerospace, large and heavy cabin segments present a significant challenge in assembling space engines. The substantial inertial force of cabin segments' mass often leads to unexpected motion during docking, resulting in segment collisions, making it challenging to ensure the accuracy and quality of engine segment docking. While traditional manual docking leverages workers' expertise, the intensity of the labor and low productivity are impractical for real-world applications. Human-robot collaboration can effectively integrate the advantages of humans and robots. Parallel robots, known for their high precision and load-bearing capacity, are extensively used in precision assembly under heavy load conditions. Therefore, human-parallel-robot collaboration is an excellent solution for such problems. In this paper, a framework is proposed that is easy to realize in production, using human-parallel-robot collaboration technology for cabin segment docking. A fractional-order variable damping admittance control and an inverse dynamics robust controller are proposed to enhance the robot's compliance, responsiveness, and trajectory tracking accuracy during collaborative assembly. This allows operators to dynamically adjust the robot's motion in real-time, counterbalancing inertial forces and preventing collisions between segments. Segment docking assembly experiments are performed using the Stewart platform in this study. The results show that the proposed method allows the robot to swiftly respond to interaction forces, maintaining compliance and stable motion accuracy even under unknown interaction forces.

Compliant gait control method based on CVSLIP-FF model for biped robot walking over uneven terrain.

Bipedal walking over uneven terrain remains a challenging task due to the environmental complexity and unavoidable landing impact. To realize the stable and robust walking of biped robots, this paper proposes a compliant gait control method, which focuses on walking compliance and conducts research on two levels. In the gait generation level, a Continuous-Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (CVSLIP-FF) model is provided with the consideration of the ankle joint and compliant spring-loaded leg. Then, a CVSLIP-FF based gait generation pattern with relevant walking strategies is provided to enhance the mobility of biped robots. In the joint control level, an ankle joint admittance control strategy is applied to achieve compliant robot-environment interaction. Experimental results indicate that compared with the traditional SLIP model, the proposed method performs better adaptability to uneven terrain with a 217.77% improvement, and enables biped robots to cope with slight unknown disturbance.

Improved precise guidewire delivery of a cardiovascular interventional surgery robot based on admittance control.

PURPOSE: The development of cardiovascular interventional surgery robots can realize master-slave interventional operations, which will effectively solve the problem of surgeons being injured by X-ray radiation. The delivery accuracy and safety of interventional instruments such as guidewire are the most important issues in the development of robotic systems. Most of the current control methods are position control or force feedback control, which cannot take into account delivery accuracy and safety. METHODS: A cardiovascular interventional surgery robotic system integrated force sensors is developed. A novel force/position controller, which includes a radial basis function neural networks-based inner loop position controller and a force-based admittance outer loop controller, is proposed. Furthermore, a series of simulations and vascular model experiments are carried out to demonstrate the feasibility and accuracy of the proposed controller. RESULTS: The designed cardiovascular interventional robot is flexible to enter the target vessel branch. Experimental results indicate that the proposed controller can effectively improve the delivery accuracy of the guidewire and reduce the contact force with the vessel wall. CONCLUSIONS: The proposed controller based on radial basis function neural network and admittance control is effective in improving delivery accuracy and reducing contact force. The algorithm needs to be further validated in vivo experiments.

Preliminary study of a new macro-micro robot system for dental implant surgery: Design, development and control.

BACKGROUND: This study aims to develop a new dental implant robotic system (DIRS) to relieve the burden and enhance the quality of dental implant surgery. METHODS: The implanting actuator and system controller are two parts. The implanting actuator is designed on the basis of the RCM mechanism, with its kinematics modelled. Besides, a multi-DOF admittance control strategy and a hybrid position-admittance control strategy were designed, endowing the actuator with environmental compliance. RESULTS: In force sensing, about 97% of mixed force/torque are eliminated. Then, 30 groups of implantation are done, of which 15 groups are simple implantation, while another 15 groups are force-based implantation. The results show that the average contact force/torque can be reduced by 73.03% and 62.66%, and the peak contact force/torque can be reduced by 68.26% and 50.46%. CONCLUSIONS: The results of preliminary experiments validate the effectiveness of DIRS, which has great potential to assist dentists with higher efficiency, better quality, and lower burden.

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation.

Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control.

Compliant robotic behaviors for satellite servicing.

The demands of traditional industrial robotics differ significantly from those of space robotics. While industry requires robots that can perform repetitive tasks with precision and speed, the space environment needs robots to cope with uncertainties, dynamics, and communication delays or interruptions, similar to human astronauts. These demands make a well-suited application for compliant robotics and behavior-based programming. Pose Target Wrench Limiting (PTWL) is a compliant behavior paradigm developed specifically to meet these demands. PTWL controls a robot by moving a virtual attractor to a target pose. The attractor applies virtual forces, based on stiffness and damping presets, to an underlying admittance controller. Guided by virtual forces, the robot will follow the attractor until safety conditions are violated or success criteria are met. We tested PTWL on a variety of quasi-static tasks that may be useful for future space operations. Our results demonstrate that PTWL is an extremely powerful tool. It makes teleoperation easy and safe for a wide range of quasi-static tasks. It also facilitates the creation of semi-autonomous state machines that can reliably complete complex tasks with minimal human intervention.

Autonomous motion and control of lower limb exoskeleton rehabilitation robot.

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient's motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation. Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient's desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory. Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively.

Force-based control strategy for a collaborative robotic camera holder in laparoscopic surgery using pivoting motion.

Introduction: Laparoscopic surgery often relies on a fixed Remote Center of Motion (RCM) for robot mobility control, which assumes that the patient's abdominal walls are immobile. However, this assumption is inaccurate, especially in collaborative surgical environments. In this paper, we present a force-based strategy for the mobility of a robotic camera-holder system for laparoscopic surgery based on a pivoting motion. This strategy re-conceptualizes the conventional mobility control paradigm of surgical robotics. Methods: The proposed strategy involves direct control of the Tool Center Point's (TCP) position and orientation without any constraints associated with the spatial position of the incision. It is based on pivoting motions to minimize contact forces between the abdominal walls and the laparoscope. The control directly relates the measured force and angular velocity of the laparoscope, resulting in the reallocation of the trocar, whose position becomes a consequence of the natural accommodation allowed by this pivoting. Results: The effectiveness and safety of the proposed control were evaluated through a series of experiments. The experiments showed that the control was able to minimize an external force of 9 N to ±0.2 N in 0.7 s and reduce it to 2 N in just 0.3 s. Furthermore, the camera was able to track a region of interest by displacing the TCP as desired, leveraging the strategy's property that dynamically constrains its orientation. Discussion: The proposed control strategy has proven to be effective minimizing the risk caused by sudden high forces resulting from accidents and maintaining the field of view despite any movements in the surgical environment, such as physiological movements of the patient or undesired movements of other surgical instruments. This control strategy can be implemented for laparoscopic robots without mechanical RCMs, as well as commercial collaborative robots, thereby improving the safety of surgical interventions in collaborative environments.

Motion assistance analysis for robot-assisted tele-rehabilitation based on bilateral admittance control / 中国康复理论与实践

ObjectiveTo develop a bilateral rehabilitation robot motion assistance strategy based on admittance control, so that rehabilitation physicians can assist patients in rehabilitation training through remote teaching. MethodsA bilateral remote rehabilitation platform with upper limb terminal traction was constructed. Based on the velocity admittance control, the interactive movement between the master robot and the rehabilitation physician was realized, and the position information transmission of the master-slave robot was realized through the communication framework built. The slave robot received the position coordinates of the main robot, and drove the patient to carry out rehabilitation exercises under the attitude admittance controller. ResultsThe robot could drive the patient to accurately track the trajectory of the doctor's teaching in real time, and improve the safety and compliance of the training and human-computer interaction. ConclusionBy introducing two admittance controllers, the trajectory of the physician's end can be accurately tracked when driving the patient's movement from the robotic arm, which effectively avoids the discomfort of the patient's arm in process of rehabilitation.

Human-Robot Cooperative Strength Training Based on Robust Admittance Control Strategy.

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human-robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human-robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism.

Design of a "Cobot Tactile Display" for Accessing Virtual Diagrams by Blind and Visually Impaired Users.

Access to graphical information plays a very significant role in today's world. Access to this information can be particularly limiting for individuals who are blind or visually impaired (BVIs). In this work, we present the design of a low-cost, mobile tactile display that also provides robotic assistance/guidance using haptic virtual fixtures in a shared control paradigm to aid in tactile diagram exploration. This work is part of a larger project intended to improve the ability of BVI users to explore tactile graphics on refreshable displays (particularly exploration time and cognitive load) through the use of robotic assistance/guidance. The particular focus of this paper is to share information related to the design and development of an affordable and compact device that may serve as a solution towards this overall goal. The proposed system uses a small omni-wheeled robot base to allow for smooth and unlimited movements in the 2D plane. Sufficient position and orientation accuracy is obtained by using a low-cost dead reckoning approach that combines data from an optical mouse sensor and inertial measurement unit. A low-cost force-sensing system and an admittance control model are used to allow shared control between the Cobot and the user, with the addition of guidance/virtual fixtures to aid in diagram exploration. Preliminary semi-structured interviews, with four blind or visually impaired participants who were allowed to use the Cobot, found that the system was easy to use and potentially useful for exploring virtual diagrams tactually.

Design and Implementation of a Rehabilitation Upper-limb Exoskeleton Robot Controlled by Cognitive and Physical Interfaces.

This paper presents an upper limb exoskeleton that allows cognitive (through electromyography signals) and physical user interaction (through load cells sensors) for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury. For the exoskeleton to be easily accepted by patients who suffer from a neurological injury, we used the ISO9241-210:2010 as a methodology design process. As the first steps of the design process, design requirements were collected from previous usability tests and literature. Then, as a second step, a technological solution is proposed, and as a third step, the system was evaluated through performance and user testing. As part of the technological solution and to allow patient participation during the rehabilitation process, we have proposed a hybrid admittance control whose input is load cell or electromyography signals. The hybrid admittance control is intended for active therapy exercises, is easily implemented, and does not need musculoskeletal modeling to work. Furthermore, electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human-robot interaction.

Human-robot-robot cooperative control using positioning robot and 1-DOF traction device for robot-assisted fracture reduction system.

While performing musculoskeletal long bone fracture reduction surgery, assistant surgeons can often suffer from physical fatigue as they provide resistance against the tension from surrounding muscles pulling on the patient's broken bones. These days, robotic systems are being actively developed to mitigate this physical workload by realigning and holding these fractured bones for surgeons. This has led to one consortium proposing the development of a robot-assisted fracture reduction system consisting of a 6-DOF positioning robot along with a 1-DOF traction device. With the introduction of the 1-DOF traction device, the positioning robot does not have to fight these contraction forces so can be compact improving its maneuverability and overall convenience; however, considering surgeon-robot interactions, this approach adds the requirement of controlling two different types of robots simultaneously. As such, an advanced cooperative control methodology is required to control the proposed bone fracture reduction robot system. In this paper, a human-robot-robot cooperative control (HRRCC) scheme is proposed for collaboration between the surgeon, the positioning robot, and the traction device. First, the mathematical background of this HRRCC scheme is provided. Next, we describe a series of experiments that show how the proposed scheme facilitates a reduction in the load placed on the positioning robot from strong muscular contraction forces making it possible to conduct fracture reduction procedures more safely despite the muscular forces.

Continuous mode adaptation for cable-driven rehabilitation robot using reinforcement learning.

Continuous mode adaptation is very important and useful to satisfy the different user rehabilitation needs and improve human-robot interaction (HRI) performance for rehabilitation robots. Hence, we propose a reinforcement-learning-based optimal admittance control (RLOAC) strategy for a cable-driven rehabilitation robot (CDRR), which can realize continuous mode adaptation between passive and active working mode. To obviate the requirement of the knowledge of human and robot dynamics model, a reinforcement learning algorithm was employed to obtain the optimal admittance parameters by minimizing a cost function composed of trajectory error and human voluntary force. Secondly, the contribution weights of the cost function were modulated according to the human voluntary force, which enabled the CDRR to achieve continuous mode adaptation between passive and active working mode. Finally, simulation and experiments were conducted with 10 subjects to investigate the feasibility and effectiveness of the RLOAC strategy. The experimental results indicated that the desired performances could be obtained; further, the tracking error and energy per unit distance of the RLOAC strategy were notably lower than those of the traditional admittance control method. The RLOAC strategy is effective in improving the tracking accuracy and robot compliance. Based on its performance, we believe that the proposed RLOAC strategy has potential for use in rehabilitation robots.

Application of Adaptive and Switching Control for Contact Maintenance of a Robotic Vehicle-Manipulator System for Underwater Asset Inspection.

The aim of this study is to design an adaptive controller for the hard contact interaction problem of underwater vehicle-manipulator systems (UVMS) to realize asset inspection through physical interaction. The proposed approach consists of a force and position controller in the operational space of the end effector of the robot manipulator mounted on an underwater vehicle. The force tracking algorithm keeps the end effector perpendicular to the unknown surface of the asset and the position tracking algorithm makes it follow a desired trajectory on the surface. The challenging problem in such a system is to maintain the end effector of the manipulator in continuous and stable contact with the unknown surface in the presence of disturbances and reaction forces that constantly move the floating robot base in an unexpected manner. The main contribution of the proposed controller is the development of the adaptive force tracking control algorithm based on switching actions between contact and noncontact states. When the end effector loses contact with the surface, a velocity feed-forward augmented impedance controller is activated to rapidly regain contact interaction by generating a desired position profile whose speed is adjusted depending on the time and the point where the contact was lost. Once the contact interaction is reestablished, a dynamic adaptive damping-based admittance controller is operated for fast adaptation and continuous stable force tracking. To validate the proposed controller, we conducted experiments with a land robotic setup composed of a 6 degrees of freedom (DOF) Stewart Platform imitating an underwater vehicle and a 7 DOF KUKA IIWA robotic arm imitating the underwater robot manipulator attached to the vehicle. The proposed scheme significantly increases the contact time under realistic disturbances, in comparison to our former controllers without an adaptive control scheme. We have demonstrated the superior performance of the current controller with experiments and quantified measures.

A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly.

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

Variable Admittance Control Based on Human-Robot Collaboration Observer Using Frequency Analysis for Sensitive and Safe Interaction.

A collaborative robot should be sensitive to the user intention while maintaining safe interaction during tasks such as hand guiding. Observers based on the discrete Fourier transform have been studied to distinguish between the low-frequency motion elicited by the operator and high-frequency behavior resulting from system instability and disturbances. However, the discrete Fourier transform requires an excessively long sampling time. We propose a human-robot collaboration observer based on an infinite impulse response filter to increase the intention recognition speed. By using this observer, we also propose a variable admittance controller to ensure safe collaboration. The recognition speed of the human-robot collaboration observer is 0.29 s, being 3.5 times faster than frequency analysis based on the discrete Fourier transform. The performance of the variable admittance controller and its improved recognition speed are experimentally verified on a two-degrees-of-freedom manipulator. We confirm that the improved recognition speed of the proposed human-robot collaboration observer allows us to timely recover from unsafe to safe collaboration.

Design and Calibration of Robot Base Force/Torque Sensors and Their Application to Non-Collocated Admittance Control for Automated Tool Changing.

Robotic manipulators physically interacting with their environment must be able to measure contact forces/torques. The standard approach to this end is attaching force/torque sensors directly at the end-effector (EE). This provides accurate measurements, but at a significant cost. Indirect measurement of the EE-loads by means of torque sensors at the actuated joint of a robot is an alternative, in particular for series-elastic actuators, but requires dedicated robot designs and significantly increases costs. In this paper, two alternative sensor concept for indirect measurement of EE-loads are presented. Both sensors are located at the robot base. The first sensor design involves three load cells on which the robot is mounted. The second concept consists of a steel plate with four spokes, at which it is suspended. At each spoke, strain gauges are attached to measure the local deformation, which is related to the load at the sensor plate (resembling the main principle of a force/torque sensor). Inferring the EE-load from the so determined base wrench necessitates a dynamic model of the robot, which accounts for the static as well as dynamic loads. A prototype implementation of both concepts is reported. Special attention is given to the model-based calibration, which is crucial for these indirect measurement concepts. Experimental results are shown when the novel sensors are employed for a tool changing task, which to some extend resembles the well-known peg-in-the-hole problem.