Freedom Robot Research Articles

Design and control of an image-guided robot for spine surgery in a hybrid OR.

Minimally invasive spine (MIS) fusion surgery requires image guidance and expert manual dexterity for a successful, efficient, and accurate pedicle screw placement. Operating room (OR)-integrated robotic solution can provide precise assistance to potentially minimize complication rates and facilitate difficult MIS procedures. A 5-degrees of freedom robot was designed specifically for a hybrid OR with integrated surgical navigation for guiding pedicle screw pilot holes. The system automatically aligns an instrument following the surgical plan using only instrument tracking feedback. Contrary to commercially available robotic systems, no tracking markers on the robotic arm are required. The system was evaluated in a cadaver study. The mean targeting error (N = 34) was 1.27±0.57 mm and 1.62±0.85°, with 100% of insertions graded as clinically acceptable. A fully integrated robotic guidance system, including intra-op imaging, planning, and physical guidance with optimized robot design and control, can improve workflow and provide pedicle screw guidance with less than 2 mm targeting error.

A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot.

In view of the urgent need for intelligent rehabilitation equipment for some disabled people, an intelligent, upper limb rehabilitation training robot is designed by applying the theories of artificial intelligence, information, control, human-machine engineering, and more. A new robot structure is proposed that combines the use of a flexible rope with an exoskeleton. By introducing environmentally intelligent ergonomics, combined with virtual reality, multi-channel information fusion interaction technology and big-data analysis, a collaborative, efficient, and intelligent remote rehabilitation system based on a human’s natural response and other related big-data information is constructed. For the multi-degree of the freedom robot system, optimal adaptive robust control design is introduced based on Udwdia-Kalaba theory and fuzzy set theory. The new equipment will help doctors and medical institutions to optimize both rehabilitation programs and their management, so that patients are more comfortable, safer, and more active in their rehabilitation training in order to obtain better rehabilitation results.

Manipulación de 7 servomotores con FPGA iCEstick Evaluation Kit

Objective. Configure a FPGA card for the manipulation of 7 servomotors of the Futaba s3003 type because these servomotors are widely used in the teaching of control and robotics. This configuration is done through the creation of various modules that allow the integration of it in various control processes. Contribution. The interface, in which the parametric and non-parametric blocks together integrate the operation of the 7 servomotors are developed, are executed in Icestudio which is a free access platform. One of these applications of this final module can be applied to a 6 degree freedom robot with final effector whose servomotors area of the Futaba s3003 type.

Impulsive control and stability analysis of biped robot based on virtual constraint and adaptive optimization

This article is concerned with the problem of asymptotically stable periodic walking for a five‐link underactuated biped robot. Because of instantaneous change of two legs and complex dynamics during the walking process, the robot can be regarded as a nonlinear impulsive system. In order to make impulsive control of the robotic system, it is modeled as a rigid kinematic chain with Lagrange equations, which is strong coupling and hybrid. A novel state feedback controller is proposed to generate a closed‐loop periodic walking gait and track the desired trajectory of each joint. This controller is designed based on virtual constraint and hybrid zero dynamics. When the holonomic constraint of each actuator is zeroed by the controller, it can be regarded as an output. By using this method, the computation of the standard 5 degree of freedom (DOF) robot model can be reduced to 1 DOF. In addition, an adaptive optimization scheme is further investigated, which solves the problem of model parameter uncertainty. Finally, simulations are presented to illustrate the effectiveness of the proposed method.

Diversity Maintenance for Efficient Robot Path Planning

Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.

Robust motion control for multi-split transmission line four-wheel driven mobile operation robot in extreme power environment

PurposeIn the multi-splitting transmission lines extreme power environment of ultra-high voltage and strong electromagnetic interference, to improve the trajectory tracking and stability control performance of the robot manipulator when conduct electric power operation, and effectively reduce the influence of disturbance factors on the robot motion control, this paper aims to presents a robust trajectory tracking motion control method for power cable robot manipulators based on sliding mode variable structure control theory.Design/methodology/approachThrough the layering of aerial-online-ground robot three-dimensional control architecture, the robot joint motion dynamic model has been built, and the motion control model of the N-degrees of freedom robot system has also been obtained. On this basis, the state space expression of joint motion control under disturbance and uncertainty has been also derived, and the manipulator sliding mode variable structure trajectory tracking control model has also been established. The influence of the perturbation control parameters on the robot motion control can be compensated by the back propagation neural network learning, the stability of the controller has been analyzed by using Lyapunov theory.FindingsThe robot has been tested on a analog line in the lab, the effectiveness of sliding mode variable structure control is verified by trajectory tracking simulation experiments of different typical signals with different methods. The field operation experiment further verifies the engineering practicability of the control method. At the same time, the control method has the remarkable characteristics of sound versatility, strong adaptability and easy expansion.Originality/valueThree-dimensional control architecture of underground-online-aerial robots has been proposed for industrial field applications in the ubiquitous power internet of things environment (UPIOT). Starting from the robot joint motion, the dynamic equation of the robot joint motion and the state space expression of the robot control system have been established. Based on this, a robot closed-loop trajectory tracking control system has been designed. A robust trajectory tracking motion control method for robots based on sliding mode variable structure theory has been proposed, and a sliding mode control model for the robot has been constructed. The uncertain parameters in the control model have been compensated by the neural network in real-time, and the sliding mode robust control law of the robot manipulator has been solved and obtained. A suitable Lyapunov function has been selected to prove the stability of the system. This method enhances the expansibility of the robot control system and shortens the development cycle of the controller. The trajectory tracking simulation experiment of the robot manipulator proves that the sliding mode variable structure control can effectively restrain the influence of disturbance and uncertainty on the robot motion stability, and meet the design requirements of the control system with fast response, high tracking accuracy and sound stability. Finally, the engineering practicability and superiority of sliding mode variable structure control have been further verified by field operation experiments.

A new image-based visual servoing method with velocity direction control

In image-based visual servoing (IBVS), for more satisfying trajectories, the target is always expected to move in straight lines to the desired positions. In this paper, a new IBVS controller for trajectory adjustment is obtained by optimizing a multi-objective function about adjusting a direction in the image plane. By applying the control law based on the optimized parameters, less redundant motion can be achieved in contrast to the traditional methods. Meanwhile, it will be beneficial for reducing the risk of the target leaving the field of view (FOV) and the robustness to the uncertainties of depth and camera internal parameters remains. The experimental results of the 6 degrees of freedom (DOF) robot with eye-in-hand configuration demonstrate the effectiveness and practicability of the proposed method.

Intuitive Bilateral Teleoperation of a Cable-driven Parallel Robot Controlled by a Cable-driven Parallel Robot

Intuitive remote multiple degrees of freedom (DOF) robot control has distinct advantages in field robotics applications, especially for a cable-driven parallel robot (CDPR) capable of operating in a wide workspace with a heavy payload. This study proposes a novel intuitive teleoperation method for a CDPR, in which a remote CDPR is teleoperated by a local CDPR driven by a human. The proposed system comprises a master CDPR in the operating room, a slave CDPR in the workplace, wireless communication between the master and slave CDPRs, and an admittance control to realize the haptic force to the master CDPR. The master CDPR transmits the Cartesian posture command, based on an operator???s manipulation, to the slave CDPR, and the slave CDPR???s wrench force obtained through the cable tension measurement is fed back to the master CDPR. For validation, two CDPRs in different locations (approximately 7.46 km apart) are utilized; a mini-size CDPR is used as the master device and a grand-size CDPR is used as the slave robot. The experimental results demonstrate that the operator can fully control the slave robot???s six DOF motions with spatial force feeling in hand, and satisfactory synchronized movements of the two CDPRs at a distance could be performed. Also, a preliminary experimental result of the proposed wrench feedback method is discussed. The proposed method has practical benefits in field robotics applications in unmanned and hazardous workspaces, such as nuclear power plants and space, which require long-distance remote manipulation at different locations.

Deep Learning Aided Dynamic Parameter Identification of 6-DOF Robot Manipulators

Generally, structural uncertainty of the robot dynamics system refers to model error caused by parameter identification, unstructured uncertainty is the unmodeled dynamic characteristic. No matter how elaborate modeling methods are used, there always be uncertainty. Therefore, this article applies deep learning for the first time to aid robot dynamic parameter identification of 6 degrees of freedom robot manipulator for compensation of uncertain factors. Firstly, the relatively accurate prediction of torque is obtained by the physical dynamic model using the parameters identification method (errors are less than 10% of the maximum torques). Secondly, we propose a novel deep neural network based approach called Uncertainty Compensation Model (UCM) to compensate the torque error introduced by the uncertainty. The UCM mainly composed by proposed Input Control Module (ICM) and Error Learning Models (ELMs) based on Long-Short-Term Memory and attention mechanism. The proposed ICM, which effectively avoids the unnecessary interference, is used to control valid input for ELMs. The ELMs, consisted by ELM units, concern extracting salient features from sequence data to predict the joint error. Also, this article summarizes the effects of valid input, timestep and attention mechanism on the performance of the UCM. Finally, the verification of parameter identification and torques compensation is carried out by a Universal Robot 5 manipulator. Compared with the prediction torques of physical dynamic model, the proposed UCM has a good ability to capture the friction characteristics and compensate for the error of local maximum torques, which effectively solves the deficiency of the physical dynamics model and improves prediction accuracy (errors are less than 6% of the maximum torques).

Robust Control of Multi Degree of Freedom Robot Based on Disturbance Observer of Neural Network

Robust Control of Multi Degree of Freedom Robot Based on Disturbance Observer of Neural Network

IOC Based Trajectory Generation to Increase Human Acceptance of Robot Motions in Collaborative Tasks

Abstract Collaboration between humans and robots is an important aspect of Industry 4.0. It can be improved by incorporating human-like characteristics into robot motion planning. It is assumed that humans move optimal with respect to a certain objective or cost function. To find this function, also for a robot, we use an inverse optimal control approach identifying what linear weighted combination of physically interpretable cost functions best mimics human point-to-point motions. A bi-level optimization is used, where the upper level compares the optimal robot result of the lower level with human reference motions. Two depth cameras are combined in a setup to record these reference motions. The resulting weighted cost functions are then used to generate new motions for a seven degrees of freedom robot arm. The resulting optimized motions are compared to standard robot motions based on linear interpolation in joint or task space. The comparison is performed by means of a small experiment where preliminary observations show that humans experience these motions as more anthropomorphic and feel at least equally comfortable and safe compared to existing motion planning strategies.

Orthognathic Surgical Robot With a Workspace Limitation Mechanism

Impracticable workspaces and cumbersome hardware limit the practical application of the currently available orthognathic surgical robots in a clinical environment. In this study, a compact and lightweight six degree of freedom (DOF) robot with a workspace limitation mechanism (WLM) was developed, and this article reports on the technical specifications of the robot, as well as the details of the three experiments performed to validate it. This robot can be easily installed by a single individual and is able to mechanically protect important organs from unintended injury. The results show that this robot functionally limits the workspace travel of the osteotomy device by an average overshoot of 0.8 mm, with a drilling and cutting accuracy of 1 and 0.5 mm, respectively. This operational accuracy has the potential to be further improved, making this novel robot a potentially precise and safe medical equipment for orthognathic surgery.

Adaptive Switch Image-based Visual Servoing for Industrial Robots

In this paper, an adaptive switch image-based visual servoing (IBVS) controller for industrial robots is presented. The proposed control algorithm decouples the rotational and translational camera motions and decomposes the IBVS control into three separate stages with different gains. This method can increase the system response speed and improve the tracking performance of IBVS while the proposed adaptive law deals with the uncertainties of the monocular camera in eye-in-hand configuration. The stability of the designed controller is proved using Lyapunov method. Experimental results on a 6 degree of freedom (DOF) robot show the significant enhancement of the control performance over other IBVS methods, in terms of the response time and tracking performance. Also the designed visual servoing controller demonstrates its capability to overcome some of the inherent drawbacks of IBVS, such its inability to perform a 180° camera rotation around its center.

Virtual reality human-robot collaborative welding: A case study of weaving gas tungsten arc welding

To combine the advantages of humans (adaptive intelligence) and robots (higher movement accuracy and physical limitation), we propose a virtual reality human-robot collaborative welding system which allows them to collaborate with each other to complete welding tasks. A common welding method, weaving gas tungsten arc welding (GTAW) is presented as a case study. In this system, a 6-degree of freedom (6-DoF) robot UR-5 carrying the welding torch works as the final performer of weaving welding. A virtual welding environment based on HTC VIVE is built where humans can observe the working scene via a motion-tracked headset without being onsite physically. A mapping model between arc voltage, arc length and welding current is established, and based on this model a seam tracking algorithm is developed which allows the robot to track the weld seam automatically. The robot is pre-programmed to weave the welding torch across the weld seam. The robot travelling along the weld seam is fully controlled by the human operator via motion-tracked handle per the observed working scene. This teleoperation style allows the humans to adjust the travel speed adaptively and freely as needed without suffering onsite danger. The proposed collaborative welding system combines the advantages of humans and robots together, utilizing the intelligence and adaptability of the human operator with precise movement obtained from robots. The welding experiments show that the welded workpiece from human-robot collaboration has better performance compared with that from either humans or robots separately and demonstrated the effectiveness of the proposed virtual reality human-robot collaborative welding system.

Asymptotical stability contouring control of dual‐arm robot with holonomic constraints: modified distributed control framework

The authors analyse the contour error dynamics in contouring control of dual-arm robotic manipulators with holonomic constraints. With the modified dynamics of robot, the dynamics of dual-arm robot with holonomic constraints is symetrically transformed into dynamics error problem by considering the equivalent error method. It becomes the control problem of stabilisation such that it is suitable for robust control approach to improve control performance in terms of contouring control, i.e. contour accuracy with high speed. The proposed method can deal with both analytic and non-analytic functions of desired path in task space. The experimental results reveal that the proposed method yields excellent performance in comparison to conventional distributed control. As a result, the proposed method can lead to the development of a modified distributed control for high degree of freedom robot manipulators.

Pilot study of a surgical robot system for zygomatic implant placement

Zygomatic implant technology has been regarded as an alternative treatment to massive grafting surgery in the severe atrophic maxillary. Nowadays, the assistant method with a real-time surgical navigation has been applied to reduce the risks of zygomatic implant placement. However, the accuracy of the complex operation is highly dependent on the experience of the surgeon. In order to avoid disadvantages of traditional surgical navigation systems, a novel surgical robot system for the zygomatic implant placement has been designed and developed. Firstly, the drilling trajectory of the zygomatic implant placement is designed through the pre-operative planning system. Secondly, the real-time positions of the surgical instruments are constantly updated with the guidance of the optical tracker. Finally, through a coordinate transformation algorithm, the drilling performance can be conducted with the control of a six degree of freedom robot. In order to evaluate the accuracy of the robot, phantom experiments had been carried out. The angle, entry point and exit point deviation of the robotic system are 1.52 ± 0.58°, 0.79 ± 0.19 mm, and 1.49 ± 0.48 mm, respectively. Meanwhile, a comparison between the robotic and manual operation demonstrates that the use of the surgical robot system for the zygomatic implant placement can improve the accuracy of the operation.

Research on Motion Path Planning of Six Degrees of Freedom Robot Based on Fuzzy Control Algorithm

Aiming at the trajectory planning problem of six-degree-of-freedom industrial robot in fixed space, this paper extends the trajectory optimization to the actuator control optimization of each joint of industrial robot. At the same time, the control algorithm of each joint steering gear of the robot under the specified path is given. Finally, the whole control strategy is simulated by the software of matlab, and the simulation results are good. The trajectory planning method of six-degree-of-freedom industrial robots proposed in this paper can be used for reference in general industrial process control.

Design and Construction of a Four-Degree Freedom Robot with PID Controller

Design and Construction of a Four-Degree Freedom Robot with PID Controller

Improvement of sliding mode controller by using a new adaptive reaching law: Theory and experiment

In this paper, a new sliding mode control (SMC) is applied to a physical nonlinear system. The novelty of this approach is related to the proposed reaching law by overcoming the main limitations of SMC. Unlike existing reaching laws, the suggested one can achieve high performance with significant reducing of a chattering problem and has a very fast convergence time of the system trajectories into the origin. This law benefits from the advantages and overcomes the limitations of both the exponential reaching law (ERL) and the conventional sliding mode control (SMC). Simulation results and comparison study with ERL and SMC are presented and applied on two degrees of freedom robot in order to show the advantage of the proposed adaptive reaching law. Experiments results are performed with electric cylinder (DC Motor) to confirm this proposition in real-time implementation.

Torque control based direct teaching for industrial robot considering temperature-load effects on joint friction

Purpose The purpose of this paper is to design a practical direct teaching method for the industrial robot with large friction resistance and gravity torque but without expensive force/torque sensor, where the gravity torque is just a function of joints position, whereas the friction is closely associated with joint velocity, temperature and load. Design/methodology/approach In the teaching method, the output torque of joint motor is controlled through current to compensate gravity torque completely and friction resistance incompletely. Three variables closely associated with friction are investigated separately by experiment and theoretical analysis, and then a comprehensive friction model which is used to calculate the required compensated friction torque is proposed. Finally, a SIASUN 7 degrees of freedom robot was used to verify the model and the method. Findings Experimental results demonstrated that the teaching method enables an operator to teach the robot in joint space by applying small force and torque on either end-effector or its body. The friction investigation suggests that the velocity and temperature have a strong nonlinear influence on viscous friction, whereas load torque significantly influences the Coulomb friction linearly and causes a slight Stribeck effect. Originality/value The main contribution includes the following: a practical joint space direct teaching method for a common industrial robot is developed, and a friction model capturing velocity, temperature and load for robot joints equipped with commercialized motors and harmonic drives is proposed.