The field of teleoperation with force telepresence has expanded its scope to include manipulation at different scales and in virtual worlds, and the key component of which is force feedback hand controller. This paper presents a novel force feedback hand controller system, including a 3-dof translational and 3-dof rotational hand controllers, respectively, to implement position and posture teleoperation of the robot end effector. The 3-dof translational hand controller adopts innovative three-axes decoupling structure based on the linear motor; the 3-dof rotational hand controller adopts serial mechanism based on three-axes intersecting at one point, improving its overall stiffness. Based on the kinematics, statics, and dynamics analyses for two platforms separately, the system applies big closed-loop force control method based on the zero force/torque, improving the feedback force/torque accuracy effectively. Experimental results show that self-developed 6-dof force feedback hand controller has good mechanical properties. The translational hand controller has the following advantages: simple kinematics solver, fast dynamic response, and better than 0.05 mm accuracy of three-axis end positioning, while the advantages of the rotational hand controller are wide turning space, larger than 1 Nm feedback, greater than 180 degrees of operating space of three axes, respectively, and high operation precision.
At present, the robot systems are widely used in hazardous operations, disaster, nuclear environment, deep sea, space, and other areas, which can replace humans in dangerous and extreme conditions to complete the tasks [
Due to the combination of human decision-making capacity and operational capability of robots in hazardous environment, teleoperation can complete complex and special tasks while making people away from the site [
According to the structure form, hand controller is divided into serial type, parallel type, and compound type [
At first, hand controller is often designed for a specific application [
This paper presents a novel force feedback hand controller. The 6-dof hand controller can be divided into a 3-dof orthogonal parallel translational platform and a 3-dof serial rotational platform. The former has large workspace and high stiffness, and the latter takes full account of the comfort while operating, the complexity of control algorithm, etc. The paper makes kinematics, statics, and dynamics analyses on the two platforms, respectively. On this basis, using servomotors and force/torque sensors produce the detection/drive module. What is more, a big force closed-loop control method based on the force sensor and a torque real time control method based on the speed adjustment are presented. In the end, the effectiveness of the control method is verified by experiment results which show that 6-dof force feedback hand controller obtaining desirable force telepresence has good mechanical properties.
For meeting the basic requirements of the movement, hand controller system should have 6 degrees of freedom [
Picture of translational platform under SolidWorks.
Picture of rotational platform under SolidWorks.
Translational platform is a 3-dof orthogonal mechanism, which is composed of the base and three branched chains. Drive components are three linear motors installed on the orthogonal bases separately and detection units are three grating rulers matched with linear motor platforms. Three motors are pairwise orthogonal and each branched chain consists of a sliding pair, three revolute pairs, and two member bars. The axis of sliding pair is parallel with that of revolute pairs. This mechanism has the advantages of simple structure, decoupling branched chains, no singularity in operating space, simple kinematic analysis, and easy control.
Rotational platform is a 3-dof serial mechanism which is composed of the base and three branched chains. The handle is installed in the center of an edge that is a part of the end of the branched chains. Drive components are three motors which are in the coaxial direction with the revolute pairs separately and detection units are encoders installed coaxially with the rotating direction of the branched chains. Three serial axes of the revolute pairs intersect at one point vertically. The structure is simple and can control the rotation angle through large angle. Each branched chain is directly installed with an encoder, so the clearance generated after using the planetary gear reducer can be eliminated and the rotation angle can also be measured accurately.
In order to make the working range of hand controller cover that of hands as far as possible, the workspace of translational platform is designed to work between ±280 mm and ±320 mm. In theory rotational platform is able to achieve ± 180° rotation with any one of the three degrees of freedom. The grating ruler and encoder should detect the change of hand’s position and posture in real time. The measurement accuracy, in general, is higher than the control accuracy. But for hand controller system, as the result of detection is the change of hand’s position and posture, it is satisfactory that the measurement accuracy adapts to the kinematic accuracy of hands sufficiently; namely, the millimeter is all right. Therefore, the detection accuracy of a 3-dof translation is in the millimeter level and that of a 3-dof rotation can be ±0.1°. The scope of force feedback is a very important parameter because it directly affects the feeling of operators and the judgment on the slave environment. The hand controller system adopts the force closed-loop control method that makes the institutions to follow the movement of hands smoothly, so operators will not receive obvious force feelings in no force feedback stage. Finally, the force and torque feedback are limited within the scope that hands have obvious feelings and the long time operation will not produce fatigue.
The translational platform drive components: linear motor is mounted on a base with which the
First, the positive and inverse kinematic solutions of the translational platform are calculated. Because of the exactly same situation of the three-branched chains, this paper will analyze the branched chain on the
In the above equation,
As for the speed analysis of the translational platform, it is important to know the relationship between its Jacobian matrix and the speed of the end and the drive. With the differential equations, the speed relationship between the motion platform and the linear motor block is deduced:
In this equation, Jacobian matrix
The last part is the statics analysis. Through the statics analysis, there is an overview of the mechanism force and the capacity against load. Using the virtual work principle can elicit the relationship between the driving force
According to the principle of virtual work, the virtual work, which is produced by the virtual displacement of the sliding pairs caused by the output force of the drive parts, is equal to the one produced by the bottom motion platform virtual displacement; namely,
Through the kinematic analysis, there is a one-to-one relationship between the displacements of the linear motor slider and the bottom motion platform:
So the statics equation of translational platform is as follows:
The left of (
Rotational platform belongs to a 3-dof serial mechanism, so it just provides the posture information of the slave robot. Meanwhile, due to the decoupling design, the translational platform has no influence on the end posture of the slave robot, only the posture information of the rotational mechanism on the static platform is required. The positive solution is provided by the
Three-axis intersection serial mechanism.
A data table, as shown in Table
Member bars |
|
|
|
|
---|---|---|---|---|
1 | 0 | 0 |
|
0 |
2 | 0 | 90 | 0 | 90 |
3 | 0 | 90 | 0 | 0 |
Using the parameters shown in Table
Therefore,
It is only necessary to analyze the inverse kinematics solution of the rotational platform to meet actual requirements, as (
In addition,
Therefore,
Then,
It is known that
Bring it back to (
So far there are four groups of solutions of
In terms of the statics analysis of the rotational platform, assuming that the output torques of the motors on the three-rotary directions are
According to the kinematics, there is
The statics equation of the rotational platform will be
Teleoperation is an important human-machine interaction in the robot operation, and transparency is the characteristic that ideal teleoperation system should have, which can make operators feel the real influence of the environment [
Basic control methods based on force control can be divided into the impedance control, hybrid control, and explicit force control according to the relationship between the position, velocity, and force control variables [
Considering the above problems and planning to integrate the teleoperation and bilateral control into one control algorithm, this paper puts forward a kind of force closed-loop control method based on the force sensor. The core idea aims to adjust the output of drive parts in real time and get the desired output of the system according to the actual stress of hands detected by the force sensor. Control structure diagram is as shown in Figure
Force closed-loop control structure.
Getting the difference
The reason for using the PID controller is that it does not need to establish the corresponding model for translational platform and rotational platform. If using the impedance control, it is necessary to confirm the inertia coefficient, damping coefficient, and stiffness coefficient, which is not easy to obtain accurately and will increase the difficulty of design and the complexity of system. PID control, however, because of its simple calculation and a good response speed guaranteed by all hardware devices, can achieve good control effect after using a certain method to adjust and select the three parameters of
After the PID controller calculates the speed variation
The hardware of hand controller system consists of the controller, detection components, and drive components. Wherein the controller includes IPC and Pmac motion control card; detection components include ATI six-dimensional force/torque sensors, grating rulers, and rotary encoders; drive components include Parker linear motor and Maxon rotary motors. 3-dof translational platform and 3-dof rotational platform are shown in Figure
Translational platform and rotational platform.
The hardware composition and relationship of the whole system are shown in Figure
System composition schematic diagram.
This paper selects Windows XP as the development platform because all the hardware devices including the force sensor, data acquisition card, Elmo driver, and Pmac motion control card have good drive support and development support for this operating system, which will reduce unnecessary compatibility problems. Meanwhile, Visual Studio 2010 and C++ are selected to develop the control software. The workflow of the control program is as follows.
The flow chart of control software is shown in Figure
PC control software flow chart.
The precision of hand controller is very important to the robot teleoperation system; therefore, to design a high precision hand controller and calibrate the precision of the main hand through scientific experiments have a positive meaning. Precision calibration can improve the geometric accuracy of institutions effectively and have deep influence on the accuracy of the master-slave operation. In this paper, using laser tracking to detect the position precision of hand controller, and the experiment data, is shown in Table
The position precision of hand controller.
3-Dof moving distance of motor platform (mm) | 3-Dof tracking distance of target ball (mm) | ||
---|---|---|---|
|
|
|
|
30 | 29.983 | 30.184 | 29.886 |
30 | 29.992 | 30.065 | 30.035 |
30 | 29.943 | 30.044 | 30.015 |
30 | 29.891 | 30.065 | 30.014 |
30 | 29.846 | 30.124 | 30.012 |
30 | 29.811 | 30.061 | 30.031 |
|
|||
Relative error | 0.3% | 0.3% | 0.003% |
The force closed-loop control algorithm is tested on the translational platform. Firstly, the performance in the teleoperation stage is tested. Because the desired force value of the outer ring is 0 N in the stage, when operators control the hand controller to move, the value measured by the force sensor should also remain close to 0 N. The experiment result is shown in Figure
The movement in teleoperation stage.
In Figure
Operators may often change the operation direction, and whether the control algorithm can drive institutions to follow the movement of hands very well or not will affect the hand feel. In the experiment, the commutation ability is tested under both low speed and high speed, and the results are shown in Figures
Low-speed reverse.
High-speed reverse.
Through the conversion of semaphore, the velocity of low-speed movement is about 0.75 m/s and that of high-speed movement is about 2.4 m/s. As can be seen from Figures
In bilateral control phase, a big closed-loop force control method is used to make accurate control for the real force operators feel, and the force value measured by the force sensor should be close to the desired feedback. During the bilateral stage, this paper adds 3 N feedback forces to the
The stress in static state.
The stress in motion state.
As can be seen from Figure
In the stage of bilateral control, when the slave sends feedback force to the master, operators will immediately feel obvious force feedback and still be able to complete the scheduled operation stably and smoothly.
After the above tests, it shows that on the basis of achieving the control target of teleoperation and bilateral control stage, the two control methods integrate into one control algorithm perfectly. Teleoperation and bilateral control can take control quickly according to the values measured by the force sensor and the response time is only about 5 ms. Moreover, switching between the two stages will have no obvious time delay. Therefore, the proposed force closed-loop control algorithm based on the force sensor can meet the control requirements of the proposed hand controller system very well.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to thank their colleagues from the Robotics Research Group for helpful discussions and comments on this paper. This work is supported in part by the Fundamental Research Funds for the Central Universities (2012RC0504), Key Project of Chinese National Programs for Fundamental Research and Development (973 Program) (no. 2013CB733005), and National Natural Science Foundation of China (no. 61175080).