The objective of the second approach is to mimic a human operator’s behaviour. Behavioural cloning is a type of imitation learning, where the idea is to use supervised learning in a straightforward manner to learn from demonstrations. The need for more advanced control algorithms came from the method’s inability to adapt or to overcome problems arising from small disturbances or uncertainties (Arulkumaran, 2017). Traditional industrial robotic systems lack methods for intuitive transfer of human expertise. This translation is a challenging task (Perzylo, 2019). A combination with reinforcement learning was suggested (Hester, 2017). (Skubic, 2000) used a hybrid control model composed of a low-level forcecontrol and a higher- level discrete event control to teach robots assembly skills from human demonstration. By reacting to changes in force-based qualitative states rather than to absolute positional information, the suggested method is based on contact formations, i.e. force-based discrete states describing qualitatively the established contact. Arbitrary forces were tracked using iterative learning (Gams, 2014). (Zhang, 2003) transferred and adapted knowledge from one task to another in a human-like language of fuzzy control rules. (Khansari, 2016) extracted desired forces from data analysis. (Xu, 2007) computed forces via an algebraic function of states and constraints for a sensor-less position- and force-control. (Oba, 2016) discussed the acquisition and replication of polishing skills of a human worker represented as tool trajectory, tool posture and polishing force. These variables which were controlled independently and simultaneously formed the input to the controller. (Abu-Dakka, 2015) presented a concept for adaptive learning of contact-based manipulation tasks. The authors suggested a scheme for online modifications to match desired reference position- and force-profiles. The latter were obtained from programming by demonstration and encoded with dynamic movement primitives. (Yang, 2018) facilitated human-to-robot skill transfer through subdivision of global skills and subsequent regulation of subskills. The authors further included an estimation of human limb stiffness into their variable impedance control scheme of the robot for the skill transfer.
Nevertheless, in the usual formulation of assessment protocols, either in research or clinical environments, pos- ition and force sense are mainly evaluated separately, without accounting for their possible interactions or inter- ference [17, 26, 27]. The most commonly used protocols are based on matching tasks, where blindfolded subjects are required to match a reference joint position [21, 26, 28, 29] or a level of muscle contraction [21, 30, 31] with the same or with the other arm, either sequentially or con- currently. These protocols allowed investigating the asym- metries in the upper-limbs position [32, 33] and force  control associated with handedness and hand preferences [35, 36]. They were also used to establish indicators for in- trinsic cerebral asymmetry at functional and structural levels [31, 37–39] and to find similarity of pathways and sensory receptors between force and position sense .
The two transformation matrices found by the C++ application are written to two separate .txt files and then read by a safety application in Matlab. From the matrices the new RPY-angles and position correction for each robot are obtained. For safety reasons the two new poses are simulated and visualized using a safety application developed in Matlab. The Matlab appli- cation is developed using the Robotic toolbox for Matlab [ 10 ] by Peter Corke for plotting two generic robot using the SerialLink class that generates an object of a serial-link arm-type robot by taking the KUKA 120 Denavit-Hartenberg parameters as input. The class offers a method for checking collision between the robot object and a solid model which belongs to the class Colli- sionModel found in physical Human-Robot Interaction Workspace Analysis, Research and Evalu- ation (pHRIWARE) toolbox for solid object construction. Further, the application uses forward and inverse kinematics to compute joint configuration and poses while joint space trajectory planning is used to simulate the path between the current and wanted joint configuration. The robot kinematics used are described in chapter 2 .
Abstract Cardiac catheters allow physicians to access the inside of the heart and perform therapeutic interventions without stopping the heart or opening the chest. However, conventional manual and actuated cardiac catheters are currently unable to precisely track and manipulate the intracardiac tissue structures because of the fast tissue motion and potential for applying damaging forces. This paper addresses these challenges by proposing and implementing a robotic catheter system that use 3D ultrasound image guidance and forcecontrol to enable constant contact with a moving target surface in order to perform interventional procedures, such as intracardiac tissue ablation. The robotic catheter system, consisting of a catheter module, ablation and force sensing end effector, drive system, and image-guidance and control system, was commanded to apply a constant force against a moving target using a position-modulated forcecontrol method. The control system uses a combination of positiontracking, force feedback, and friction and backlash compensation to achieve accurate and safe catheter-tissue interactions. The catheter was able to maintain a 1 N force on a moving motion simulator target under ultrasound guidance with 0.08 N RMS error. In a simulated ablation experiment, the robotic catheter was also able to apply a consistent force on the target while maintaining ablation electrode contact with 97% less RMS contact resistance variation than a passive mechanical equivalent. In addition, the use of forcecontrol improved catheter motion tracking by approximately 20%. These results demonstrate that 3D ultrasound guidance and forcetracking allow the robotic system to maintain improved contact with a moving tissue structure, thus allowing for more accurate and repeatable cardiac procedures.
Control of active com pliant m otion is a complex problem , since the task frame directions do not correspond one-to-one to the joint-space degrees of freedom. As a result, every joint contributes in general both to the execution of m otions in the position-controlled and in the force-controlled directions of the task fram e, w hile in the end, w hatever the particular control im plem entation, only a single torque is applied at every joint. C o-ordinate transform ations have to be perform ed in the control loop. In the following few sections of this chapter, w e discuss the outer-loop forcecontrol schem e w ith an extra com pliant device, w hich will largely sim plify the problem , as the arm dynam ics can be ignored. As an example, Fig. 5.1 show s the general one-dim ensional (z direction in TOOL frame) external force-control scheme. The error betw een desired force Fd and actual force F is fed into the force controller, w hich generates position com m ands (u). The difference betw een actual robot position x and the position of the environm ent X causes a contact force via stiffness k. This contact force acts as a disturbance on the position-control loop (PUMA560 controller). The sensor dynam ics m ay be neglected if the structural resonance frequency introduced by the force sensor lies well above the position-loop bandw idth. An extra passive compliance is added to the arm sensor system , and its role in the active im pedance control is analysed in d ep th in this section.
The control architecture of the proposed UGBB robotic system is illustrated in Fig. 1. The structure is developed based on the external forcecontrol scheme which has been proved to be the best solution on safety constraints, simplicity and high rejection rates for disturbances in the actuation system , it does not cause the downside of kinematic instability , and guarantees that all directions in space are always fully controlled . In this control architecture, the actual MELFA CR1 robot controller is simulated by the positioncontrol law in MATLAB Simulink environment. At its core, the positioncontrol law consists of forward and inverse kinematics algorithms. Looking at the figure, desired Cartesian position, X d of the robot end- effector is translated to joint angle for each arms of the robot by the inverse kinematics before being fed to its SimMechanics model. The actual joint angles from the model are then translated using forward kinematics to get the actual Cartesian position of the robot end-effector, X a . The actual and desired positions are then compared to correct the robot position, X R . Both position and force controls are simultaneously realized with the position of the robot, X R being controlled by varying the desired contact force, F d .
This article describes a robotictrajectorytrackingcontrol system that verifies trajectorytracking accuracy by writing. First of all, the related dynamic model is established, and the trajectory planning algorithm is researched. Through the establishment of the host computer display interface, the protocol communication between the robot and the PC is completed, and the effect of writing and drawing (circular, heart-shaped, square, etc.) of the robot with the touch screen is achieved. The experimental results show that the robot arm can write and draw correctly, and the upper computer interface of the control system can display the real position of the end of the arm in the writing process in real time, which has real-time and good stability.
Perdereau, V., & Drouin, M. (1994). About kinematic local instability and stabilization of hybrid control schemes. In Proc. of ISRAM ’94 (pp. 545–530). Hawaii, USA. Perez-del-Pulgar, C. J., Munoz, V. F., Velasco, J. J., & Gomez, R. (2013). Parallel force- positioncontrol scheme with fuzzy gain tuning for single port laparoscopic surgery. In 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) (pp. 101–106). IEEE. http://doi.org/10.1109/ICCAS.2013.6703871 Physicians, A. C. of E. (2016). Vital Signs. Retrieved March 21, 2016, from
The kinematics analysis of agricultural vehicles is carried out and the kinematics model shown in Figure 3 is established in the plane coordinate system. The front wheel of the model turns and rear wheel drive. In the process of work, the steering wheel and the driving wheel adjust the angle and speed by adjusting the voltage. In the whole kinematics analysis, the agricultural vehicle is regarded as a rigid body running on horizontal plane. In order to determine the agricultural vehicle’s position and attitude in the whole trajectory, the navigation coordinate is set up. The center point of the test car’s rear axle was selected as a reference point to define the position and position information of the car. Pose information is defined as (x, y, φ). In which, x, y are the axial coordinates of the vehicle’s rear axle (m); φ is the heading angle (rad); δf is the front wheel steering angle (rad); v is the center speed of the rear axle (m/s); v f is the front axle center speed (m/s);
the time instant 𝑡 = 5 𝑠𝑒𝑐. as shown in figure (2). Figures (4) and (5) show the simulation results of the computed torque control system without the neural network compensator in the presence of the structured and unstructured uncertainties, re- spectively. It is clearly shown in these figures that the com- puted torque and the feed-forward (FFC) controllers were not capable of compensating for neither the parameter uncertain- ties nor the externally applied disturbance torques. This is be- cause both the computed torque and the feed-forward (FFC) controllers are designed based on mathematical models in- volving the uncertain parameters of the robotic manipulator and the actuating motor. Figure 4 shows a steady-state error in the horizontal parts of the figure of about 0.5 𝑟𝑎𝑑 for the shoulder joint motor position and about 0.04 𝑟𝑎𝑑for the el- bow joint motor position. The steady state errors in the verti- cal parts are about 0.5 𝑟𝑎𝑑 for the shoulder joint motor posi- tion and about 0.12 𝑟𝑎𝑑 for the elbow joint motor position.
The Graphical User Interface (GUI) has been designed in order to provide a simple and effective human- robotic arm interface and an easy control of the system. Figure 7 shows a screenshot of the GUI. On the left, the image captured from the camera is shown. On the top- right, the two virtual joysticks allow to control the end effector in the XYZ direction and to control the rotation of the arm. These controls are needed if the target is not included in the camera frame. In such situation, the user can simply move the arm in order to focus the desired object. Since the joysticks are required only to focus the target in the camera frame, high accuracy in the user manipulation skills is not required. On the bottom right, the last two buttons allows to power on/off the actuators.
Cen et al. 2017 discussed the effect of dynamics on forces and torques of an arm robot applied in milling industry by designing a force model. The experimental results showed that the proposed method reduced 50% to 70% error for key characteristic of the robot milling’s forces . Bruno et al. 2017 proposed the dynamic model identification of an industrial robot that is linear with respect to the parameters . Paik et al. 2012 designed a humanoid robot by taking the model of a 7DOF arm robot and an 8DOF hand. The proposed model was compatible with the narrating model humanoid, and at the same time powerful and functional to execute various task .
Patients who suffers quadriplegia level C1-C3 are unable to move their head flexibly. So, eye-tracking detection method is suggest to be used to control the movement of a motorized wheelchair. There are a few method to detect eye blink, for example using a camera to capture eye movement, or using an EEG headset that includes eye-blink detection such as NeuroSky MindWave EEG headset . Eye-blinking technique can be combined with EEG signal to increase safety as human eye could accidently blink more than usual. Some of the eye blinking frequency or eye ball position could be used as a different command as shown in Table 2.1 :
The aim of mobile robotic systems is the construction of autonomous systems that are capable of moving in real environment without the comforts of human operator by detecting objects by means of sensors or cameras and of processing this information into movement without a remote control.
Precise control of robotic manipulator is a challenging problem. Robotic manipulators have highly non linear, time varying and highly coupled dynamics. Moreover there always exist uncertainty in the system model such as external disturbance, parameter uncertainty and sensor error. The present paper has focused to improve the trajectorytracking performance of rigid robotic manipulator under uncertainties. Sliding mode controller (SMC) with its various variants such as sliding mode controller with saturation function, with hyperbolic tangent function are implemented for trajectorytracking of robot manipulator. The performance of system with payload uncertainty and disturbances with these controllers (classic PID, Sliding mode controller (SMC) and Sliding mode controller with saturation function) are compared. Simulation shows that the Sliding mode controller shows the better performance than the PID controller especially with payload uncertainty and unknown disturbances. It has also been observed that the chattering problem present in control input of SMC can be eliminated by using saturation and hyperbolic tangent functions instead of signum function in SMC. Performance of these controllers has been compared in terms of various norms of tracking error and chattering in control input.
in figure 2, which including torque signal of wheel. Function f(x,y,θ) and b(x,y,θ) are imprecise, and there are many sources of uncertainties. In figure 2, [x d ,y d ,θ d ] T indicates the desired position coordinate of the trajectory would be
In this paper, a geometric approach of inverse kinematics derivations for a new design of 7-DOF robotic hand consisting three 2-DOF fingers and 1-DOF palm is discussed. In previous work, the robotic hand has been developed from a custom mechanical design and the fabricated hardware has been successfully validated for a grasping task in real time . Subsequently, PID positioncontrol of the joint actuators has been applied for the real- time control of the finger joints . However, in these studies, the reference trajectory provided to the robot was in direct angles of the joint motors. Thus, for a complex system which involves more exterior sensor measurements, the generated trajectory reference requires the conversion of the Cartesian position to the joint configuration through the use of inverse kinematics equation of the robot. The robot hand has a unique design and dimension, therefore the derivation of the kinematics equation and the validation on its practicality are presented.
It is also noted that a small external torque of about 0.1Nm still exists in Fig. 19, which is different from that claimed in Theorem 3 and the simulation results. This may be explained by the following fact. During the experiments, we note that the human partner may change his motion intention according to robot trajectory. This is an interesting issue but was not considered when developing the proposed method. In particular, we assume implicitly that the human motion intention is stationary with respect to the actual robot trajectory, i.e., the adaptation of the robot trajectory has no effect on the human motion intention. However, human motion is also an output of the neuromuscular control system, so the dynamic interac- tion with the robot could well result in concurrent adaptations in the human motion intention. This makes the problem more tricky and it needs to be further investigated in the future work.
It can be seen from the Fig. 3 that although the initial value of the position is far away from the reference trajectory, the control scheme that proposed in this paper can hold the quadrotor UAV track the cylindrical spiral trajectory instantly and accurately, and the performance of the sliding mode ADRC is better than the classical ADRC. The control strategy proposed in this paper has faster response, higher precision, less overshoot and less errors than classical ADRC. From Fig. 4, there is a higher overshoot in position x response controlled by classical ADRC than the response controlled by sliding mode ADRC, the response speed of the proposed method in position z is faster than the classical ADRC obviously and the tracking errors of the three positions achieved by the proposed controller are lesser than the errors that achieved by classical ADRC.
Strategy is proposed for precision motion control of AC servo motor for applications which are inherently repetitive in terms of the motion trajectories. The dynamic second order transfer function model of the AC servo motor is derived and the model parameters are identified using experimental data. A New Modified Repetitive Control Strategy (NMRCS) based on zero-phase filtering is incorporated to the existing conventional PD feedback controller to enhance the trajectorytracking performance by utilizing the experience gained from the repeated execution of the same operations. The Iterative Learning Control Strategy (ILCS) based PD controller and conventional PD controller are taken for comparative studies. Experimental results are presented to reveal the practical appeal and efficiency of the proposed scheme.