Estimation of the wrist torque of robot gripper using data fusion and ANN techniques

Transcription

1 Estimation of the wrist torque of robot gripper using data fusion and ANN techniques Wu Ting 1 Tang Xue-hua 1 Li Zhu 1 School of Mechanical Engineering Shanghai Dianji Universit Shanghai 0040 China School of Mechanical Science & Engineering Huazhong Universit of Science & Technolog Wuhan China Tel.: + 86 [1] wut@sdju.edu.cn Abstract This paper proposes a ind of measurement method of wrist force/torque for robotic multi-sensor gripper. The method adopts the data fusion technique according to the output variations of the finger force sensors installed in the gripper. The finger force sensors are used to measure the clamping force of the gripper in the design. When the accurac of measurement is not required exactl and there is the limitation of weight and volume in space robots we utilize the existing devices to estimate the wrist force/torque without the wrist force/torque sensor which not onl meets the practical requirement but also decreases the weight and cost of robots. An experimental bench is developed and the calibration experiments are conducted to detect the relationship between the wrist force/torque and finger forces. The experimental data are used to train three laers of radial basis function (RBF artificial neural networ and the structure and weight values of the networ are obtained. Comparing with BP networ and single RBF networ based on the same samples this method can reduce the training time greatl and increase the estimation accurac significantl. The results of data fusion of the wrist force/torque are consistent with the practical calibration values and the effectiveness of the wrist force/torque estimating technique is proved. Kewords: Robot gripper wrist force neural networ data fusion 1. Introduction In the space robots often assist or replace the astronaut to do some tass out of the spaceship such as checing the space station grasping and operating the wor-pieces and catching the floating objects etc. When robot taes these operations the wrist of its gripper will receive force. If the force exceeds its limitation the flange at the root of gripper will be fractured even the robot probabl separates itself from the experimental platform and becomes the space rubbish [1~] this situation is ver dangerous. Therefore to ensure the safet of operation of robots it is necessar to monitor the wrist force/torque for robot gripper. In theor the wrist/torque sensor can measure the force precisel. However for the specialt of space robot the wrist force sensor sometimes can not be equipped on it because of the limitations of its weight and dimension. Can other sensors on the gripper be utilized to measure the wrist force/torque of space robot? We found that the output variations of finger force sensors can reflect the value of the wrist force/torque although their relationship is ver complex and difficult to be described using a mathematical model based on analtical methods. Therefore this paper presents a novel method that utilize artificial neural networ which possesses the good propert of approximating an functions to describe the relationship.. Multi-sensor gripper A multi-sensor gripper is shown in Fig. 1 which is developed b Hefei Institute of Intelligent Machines Chinese Academ of Sciences [4]. It consists of a gripper mechanism and a sensing sstem. The gripper mechanism is a one-dimensional actuating mechanism and the operations of open and close are controlled b a motor. A gripper has two fingers. The left finger has five clamping faces s1 s s s4 and s5 and the right finger also has five clamping faces s6 s7 s8 s9 and s10. The s1 s s s4 s6 s7 s8 and s9 form one V-shaped groove to grasp the I-shaped beam or objects. The s5 and s10 form another V-shaped groove to grasp the small objects. Eight finger force sensors can measure the force in the vertical direction along the clamping face. The outputs of eight finger force sensors show the contacting force between the gripper and objects.. Experiment of calibration Before we determine the structure of an artificial neural networ (ANN it needs a number of samples for training. So we developed an experimental platform and had the experiment of calibration. The experimental equipment consists of a robot s gripper of EMR (the extravehicular mobile robot which is developed b Institute of Intelligent Machines Chinese Academ of Sciences a flange of gripper a square experimental bench made of iron several weights a personal computer (PC and a data reception and control sstem and so on. 4-06

2 Fig. 1. Structure of gripper. Fig.. Calibration diagram of Fx (a Vertical view (b Front view. We simulate the circumstance that the space robot wals on the ground and grasps wor-piece the motor-driven maes the gripper with four fingers firml grasp the I-shaped beam which is shown in Fig.. We can acquire the outputs of eight finger force sensors when the gripper is connected safel. The outputs at this moment are recorded as the initial value. Then the flange of the gripper is exerted the forces and torques through hanging the weights on the plate at the directions of Fx F-x F F- Fz Mx M-x M M- Mz and M-z and the weights are hanged in proper order such as 0g 5.5g 10.5g 15.5g then uninstall the weights in the invert order such as 15.5g 10.5g 5.5g 0g and record the outputs of the finger force sensors at ever time. In this wa a calibration ccle in one direction is completed there are four ccles made in ever directions. So we receive a number of calibration data through dealing with it b ANN we can determine the wrist force/torque of gripper when waling on the ground or operating worpiece and provide basis for controlling the whole space robot. 4. Data fusion In this paper a method for decreasing the spatial component of the measurement error of the triangulation scheme has been described. The method is based on polarization filtration of the radiation scattered from the surface. The experimental investigations performed have given an increase in the accurac of measurements b a factor of more than two. We ever utilized the classic ANN for example BP RBF to fuse the wrist force/torque according to the output of finger force sensors [5] but it can not satisf the precision well. Therefore we design a RBF_RBF_RBF i.e. R neural networ to do the data fusion. 4.1 R neural networ R neural networ consists of laers RBF. The output of the first laer RBF is regarded as the input of the second laer RBF the output of the second laer RBF is regarded as the input of the third laer RBF the final result is the output of the third laer RBF. The structure of three RBF is same but the spread constants (SP of radial basis functions are different. There are 8 input nodes (8 outputs of the finger force sensor and 6 output nodes (the wrist force and torque in directions. The whole structure and diagram of R neural networ is shown in Fig.. Fig.. Architecture of R networ. (1 The first laer RBF The input vector P1 i.e. the input vector Pr of the whole networ has R1 elements i.e. R1 nodes that express the outputs of eight finger force sensors. An input weight matrix W1 is made of S11 rows and R1 columns [6]. dist is Euclidean distance between P1 and W11 in response the distance of the nth radial basis function is given b 4-07

3 dist ( n ( W11( n P A basis vector b11 having S1 elements and the lg b11( n SP ( n 1 The input vector n11 is the basis vector b11 multipling b dist element-b-element dist b n The output of the first laer the networ can be obtained with following formulation a exp( ( n exp( ( dist b 1 11 The vector in the linear laer is given b n W a (5 W is the weight matrix of the linear laer(w=s1 S; b1 is the basis vector of the linear laer and has S1 elements. The output of the first laer RBF networ is a b1 1 n 1 ( The second laer RBF The input of this laer is the output of the first one i.e. P1 a1 n1 Similarl it has the following expressions dist ( n ( W1( n P 1 lg b1( n SP ( n n1 dist b 1 a1 exp( ( n1 exp( ( dist b n W a1 b The output of the second laer RBF networ is a n 1 (1 ( ( (4 (6 (7 (8 (9 (10 (11 (1 ( The third laer RBF The input of this laer is the output of the second i.e. P a Similarl it also has the following expressions dist ( n ( W1( n P 1 lg b1( n SP ( n n1 dist b1 a1 exp( ( n1 exp( ( dist b n W a1 b The output of the third laer RBF networ is 1 (1 (14 (15 (16 (17 (

4 a n The output of whole networ is a (0 The training progress includes three steps: the first step is to establish and train the first laer RBF then record the architecture the second step is to establish and train the second laer RBF based on the first laer then record the architecture the third step is to establish and train the third laer RBF based on the two laers before then record the architecture. When the result satisfies with requirement or the number of nerve cell reaches the limitation training will be stopped. Ever RBF networ has 6 output nodes and the function of the two bac laers is revising output error and stabilizing the performance of networ. Comparing with monolaer RBF networ multi-laer RBF networ improves the reliabilit and stabilit obviousl. 4. Procedure of data fusion The output of eight finger sensors at the initial unloaded condition is considered as the basis values (at this time the finger sensors have outputs but the wrist has not been received force. Others outputs minus the basis values. In this wa the output variations of finger force sensors are obtained i.e. the input of the whole networ the wrist force/torque of different directions are as the output nodes. According to following two equations (1~ the input data of R networ is normalized to [-11] the output data is normalized to [0.10.9]. ( pr pr pr ih Where pr prih ih i max pr i min i min 1 h 1... H i 1... is the hth sample of the ith input node before normalization pr i max R p i min pr ih (19 (1 is the value after normalization is the max. of the ith input node and is the min. H is the total number of samples. 0.8( jmin 0.1 jmax jmin h 1...H j 1...S ( Where is the hth sample of the jth output node before normalization is the output after j max normalization is the max. of the jth output node and is the min. of the jth output node. The samples are divided into two groups. One group (A is used for training the networ the other group (B is used for testing the accurac of the data fusion. A+B=H. During the progress of training adjust the parameters continuousl especiall the spread constant of radial basis function is ver important and have a significant effect on the training result. After we get the architecture of R networ the samples of B group are sent into R networ and calculated the result of fusion. In order to compare it with the output of the samples it needs to be renormalized according to equation ( ( 0.1( j max 0.8 j min j min j min l 1...Q j 1... S is the lth ouput of jth node before renormalization is the one after renormalization. The error between the result of fusion and calibration value is calculated according equation (4 Max E jfs 100% l 1...Q j 1... S where is the ith calibration value of jth ouput node is its ith fusion result is the full scale range of the jth output node. 4. Result of fusion When calculating error different output nodes have different full measurement scale. Respectivel the scale of Fx F Fz Mx M Mz is 0.8N 0.8N 151.9N 1.67N.m 1.67N.m 15.19N.m. the jfs ( (4 4-09

5 max error in Table 1 is the max fusion error of calibration data in the same group. The motor of gripper is driven b voltage of 18V DC. Accordingl Table 1 is the result and error of fusion. Table 1. Results of fusion. result of fusion (wrist force/torque of 6 dimention Fx(N F(N Fz(N Mx(N.M M(N.M Mz(N.M max error % % % % % % % % % % % % % 5. Conclusion (1 Dealing with different problems we should use different tpes of NN. The wrist force/torque is related with its temporal station so we should select the front NN. Although the training need a long time as far as measuring the wrist force/torque to be concerned if we onl need to now the architecture of networ so we can training networ offline and fusing data online. It has no effect on calculation in real time. ( We ever adopt BP networ to solve the problem. But it indicates that the training progress taes a long time and the precise is not ver good. Because BP algorithm is characterized b grad dropping and global approximation sometimes it probabl gets into local least. However RBF networ is local approximation. It has the capabilit of fast learning and can reduce sensitivit to the order of presentation of training data. ( Although RBF networ has solved the problem of training time but the precise of fusion is not quite ideal [7]. Then we use R networ. Actuall it is combined b RBF organicall. It not onl possesses the virtues of RBF networ but also can improve the capabilit of approximation because of increasing laers of nerve cell. Most importantl the precise of fusion comparing with BP and RBF has been improved significantl. Using the same experiment data the max error of BP is % RBF is % but s % the precise exceeds far from the other two networs. (4 The spread constant (SP is one of the most important parameters in the radial basis function (RBF networ. We need adjust its value constantl so that the fusion results reach the best accurac. However the rule of adjustment is not exist it need to rectif SP based on practical experience. Usuall we select SP from 0.1~ Acnowledgements Authors would lie to than Dr. Chen Yu-wang for valuable contribution and helpful proof-reading. References 1. G. Hirzinger B. Brunner J. Dietrich and J. Heindl. Sensor-Based Space Robotics--ROTEX and It s Telerobotic features. IEEE Transactions on Robotics and Automation. 199 vol. 9 No. 5 pp K. Machida Y. Toda Y. Murase and S. Komada. Precise space telerobotic sstem using -finger multisensor hand. Proc. of the 1995 IEEE International Conference on Robotics and Automation. Part 1 (of Nagoa Japan pp K. Machida T. Mimi S. Komada. Precise EV Robot: Flight Model and telerobotic operation for ETS- VII. Proc. of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Sstems. Osaa Japan. Part (of pp D.M. Perr. Multi-axis force and torque sensing. Sensor Review vol. 17 No. pp L.-P. Chao K.-T. Chen. Shape optimal design and force sensitivit evaluation of six-axis force sensors. Sensors and Actuators A vol. 6 pp C.-F. Xie. Development of a Multi-sensor Gripper Sstem for an Extravehicular Mobile Robot. Master thesis. Hefei Institute of Intelligent Machines Chinese Academ Sciences China Q.-Li Li K.-J. Xu. Multi-sensors data Fusion based Measurement of Wrist Force for Robots. Chinese Journal of Scientific Instrument