Journal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article

Transcription

1 Available online Journal of Chemical and harmaceutical Research, 04, 6(5): Research Article ISSN : CODEN(USA) : JCRC5 Robot soccer match location rediction and the alied research of Kalman filtering algorithm Kelei Guo Deartment of hysical Education, Xianyang Normal University, Xianyang, China ABSRAC In a dynamic environment, it is not an easy task to osition the soccer robot accurately, this aer studies the roblem, to hit the dynamic football, and the robot must first determine the osition of the football and him. he results show that the Kalman filter algorithm is an algorithm to redict the location of the football and the other in the shortest ossible time, this algorithm has a very wide range of adatability. Key words: Kalman filtering algorithm, Robot Soccer, osition rediction, the otimization algorithm INRODUCION he robot soccer is more and more advanced with the develoment of the imes, among which the Navigation system is an indisensable art of robot soccer. As the robot's brain, the Navigation system acts as the role of a commander. However, for the robot soccer, only to determine their own osition, the next ste of decision making and lanning can be carried out [-5]. redecessors have made many achievements for the research of robot location [6-9]. For examle, Ma Hui et al roosed that the technique can be divided into two tyes for the research of robot soccer ositioning, including absolute and relative ositioning of the ositioning [0-3]. Both are locating the osition of the robot through geometrical relationshi, then extracting the calculation system for calculating coordinate inut. his article research is recisely establishes above the foundation which the scholar studies, Kalman filtering algorithm is established, imroved and Verified by exeriment. Finally, the exerimental results that this algorithm lays an imortant role for robot soccer develoment and imrovement. HE COMOSIION OF HE KALMAN FILERING MEHOD OF MODEL Robot soccer match is an imortant art in the field of artificial intelligence. During the match, Data acquisition is under a closed system, as shown in Figure. Figure : Soccer robot is always in the rocess of reeat the above, then the game roceed smoothly 904

2 Kelei Guo J. Chem. harm. Res., 04, 6(5): he establishment of the Kalman filtering method of the model In the early, in order to solve the roblem of noise generated by the radar, the filtering rediction theory was introduced, but it had flaws, in order to comensate for this drawback, a Kalman filter algorithm was roduced. Assumtions need to be made in order to establish the system of observation and state sace model of Kalman filtering algorithm. he state of the system can not be directly observed; he whole rocess of the influence of noise can be ignored Control inuts affect the state of the system W U Under such circumstances, reresents systematic rocedure incentive noise, reresents System control X inut, and reresents System state variables, so we can get stochastic differential equations of this status: V Z Reresents noise observation, equations of this status: X AX BU W (.) reresents the observed variables, so we can get a stochastic differential Z =CX V (.) If the two indeendent quantities V, W, R reresents Measurement noise covariance matrix, Q reresents V ~ N(0, R), W ~ N(0, Q), State transition matrix of Excitation rocess noise covariance matrix, there the system is reresented by A, Observation matrix is reresented by H. osteriori error estimation and state estimation is reresented by e X X X and e X X, Frontier state estimation and error estimation with. herefore, we can conclude that the two corresonding covariance are: E e e E e e (.4) he weighted Measuring estimate and Measurements with riori status then in the linear combination, we can get: X X K Z C X (.5) Z CX Among them, the measurement residual formula above is reresented by, it actually reflects the difference between the numerical value and the re-estimation of measurement between the actual measurement, the solving about Kaman gain is: K C C C R hrough the analysis above, we get that Kalman filter algorithm is actually a semi-closed loo control of a structure, it is the oeration of the system in the form of a feedback rocess, through above, the osterior circulation of two different estimates of the rocess and the never-ending cycle of estimates of filtering can be established. he measurement udate equations are: K C C C R (.8) X and (.3) (.6) X X K Z C X (.9) 905

3 Kelei Guo J. Chem. harm. Res., 04, 6(5): he formula about the udate of time is: A A Q X A X BU We can describe the rincile of this algorithm through a rocess flow diagram. As shown in Figure : (.0) (.) Figure :Block diagram of the Kalman filter As the starting error O ( ) X and state 0 is known. We can get an accurate estimate of the system state. HE EXENSION OF HE KALMAN FILER ALGORIHM x f x, u, w Considering the z hx, v is a nonlinear rocess, Coleman filtering and linear measurements is very similar in the rocess, hus, we can get the formula about the udate on time rediction: After correction equation, we get the udated formula: A A W Q W x f x, u,0 K C X X K Z h( x,0) K C C C V R V of the above formulas are in the fundamental change for V C W A, if the iteration time is long Each enough, the incorrectly initial offset values will be offset such that the filter converges to the best state. In this aer, the function of two linearly combined to relace the original derivative function, this is based on the rincile of filter, and the imroved rocess is: (3.) (3.) (3.3) (3.4) (3.5) X X ha K m m m f () he number of is reresented in v, while the undetermined factor are exressed in k who m need to be met is: v (3.6) a m, hen the relation 906

4 Kelei Guo J. Chem. harm. Res., 04, 6(5): m K f t c l, X lb K m m mj j j (3.7) o imrove the accuracy of rediction with the correction method of matrix to relace the observation matrix, the stes are as follows: Z h( X ) C( X )( X X ) / / / Z h( X ) g( Z, X )( X X ) / / / / * (, / ) / * (, / ) K g Z X K g Z X (3.0) g( Z, X ) / On the tye of the observation matrix is reresented in, as the correction function matrix is C ( X / ) reresented in, by the equation above, we can get that in the next ste the observation matrix is much more higher than correction matrix about the forecast accuracy. HE RESEARCH ABOU SOCCER ROBO OSIION REDICION MODEL At first will describe the cameras catured football movement with coordinate, its formula is as follows: X ( x, y, v, v ) x y So you can get the equations of the motion of football: X MX acc (3.8) (3.9) (4.) ax( tn) 0 tn tn a ( ) x tn X ax( tn) ay ( tn) (4.) In the formula above, the friction roduced by constant acceleration is reresented by a, the seed of the ball is reresented by v, direction of movement is reresented by, forecast eriod is reresented by N, t decision-making system oeration cycle is reresented by a a z y a cos, v at vx t, else asin, v at vy t, else, and We simlified its arameters according to the characteristics and rediction system of equations and combined with imroved algorithm. hen we can get: A M (4.3) (4.4) 907

5 Kelei Guo J. Chem. harm. Res., 04, 6(5): C v Q V 0 v W 0 R Zk xobs y obs In the rocess of the soccer motion state variables increase, robot soccer is very close to the football, then we can get: k ( R r) v max R, R d (4.5) In the formula above, the radius of robot soccer and the football is reresented by R and r, the distance between the closest football and the robot soccer is reresented by d, while the gain is reresented by k, they usually meet ( R r) d. HE RESEARCH ABOU HE REALIZAION ROCESS OF HE SIMULAION EXERIMEN hrough the analysis of source rogram, sending and receiving are realized with the imlement of the language C, the flow chart as shown in Figure 3: Figure 3:Using UD rotocol to obtain location information of the ball original rogram flow Next, achieve football's trajectory by alying the correlation software, the corresonding rogram diagram is shown in the following figure 4: Figure 4: Simulation rogram flow Note: A A WQ W (): K C C C V R V (): K C (3): (4): (,0) X X K Z h x obs obs (5): z x y (6):he initial value of each arameter is initialized 908

6 Kelei Guo J. Chem. harm. Res., 04, 6(5): CONFIRMAORY ES O IMROVE HE MODEL In order to verify the rationality of the imroved algorithm, this aer conducted two tests, namely robot soccer act as goalkeeer and lay soccer. First tests conducted the goalkeeer, to revent the other side of the ball into his team's goal is the resonsibility of the goalkeeer, it will make the robability of success greatly imroved if the goalkeeer knows e the location of the football in advance. able : he success rate of two algorithm's goalkeeer goal he number of successful intercet Statistical shooting times Success rate of goalkeeers Before imrove % After imroved 7 9% Since then the exeriment has been carried on about laying soccer robot, and calculated the change in algorithm resectively before and after laying soccer robot shooting. As shown in table : able : he rate of the algorithm before and after the two robots shots hit Number of hits Statistical hitting times Hit rate Before imrove % After imroved % hrough the exeriments above we can draw a conclusion : when the football robot do the goalkeeer, after imrovement the Kalman filtering algorithm, the success of the robot were increased about 5%,herefore, the imrovement of the algorithm make a contribution. to robot in hitting the ball. CONCLUSION In this aer, Kalman filtering algorithm is roosed through the study of soccer robot in laying rocess, and exounds the rinciles of this algorithm, as well as the advantages and disadvantages, in order to make u for its shortcomings, he rincile of the algorithm, this aer has resented on the imroved Kalman filtering algorithms, and ut this algorithm to the system in which the robot soccer lays, and thus established a redictive model of football state, then get the simulation results on the flow for the location of robot soccer. Finally, conducted exeriments to figure out whether the imrovement of algorithm romoting the develoment of robot soccer, then get a conclusion that the imroved Kalman filtering algorithm greatly imroved the success rate of the robot soccer games. REFERENCES []Alireza Fadaei ehrani, Ali Mohammad Doosthosseini, Hamid Reza Moballegh, eiman Amini, Mohammad Mehdi Daneshanah. RoboCu, 003, [] R.E.Kalman. ransaction of the ASME - Journal of Basic Engineering, 960, (8), [3] Carlos F. Marques, edro U. Lima. RoboCu, 000, [4] S.hrun, D.Fox, W.Burgard, and F.Dellaert. Artificial Intelligence Journal, 00, (8), [5] KAN Li-ing. Bulletin of Sort Science & echnology, 0, 9(3),9-0. [6] Zheng Wei. Sort Science and echnology, 000, (3),3-6, 33. [7] Yang Jilin et al. Journal of Shandong hysical Education Institute, 00, 8(3),5-53. [8] WANG Xin. Journal of Nanjing Institute of hysical Education, 00, 6(5), [9] ZHANG Ji, xiang. Journal of Hubei Sorts Science, 00, (),74-75, 79. [0] Li Ning, Zhou Jiandong. Journal of Jilin Institute of hysical Education, 0, 7(3), [] Xiaomin Zhang. Journal of Chemical and harmaceutical Research, 03, 5(), 8-4. [] Wang Bo; Zhao Yulin. Journal of Chemical and harmaceutical Research, 03, 5(), -6. [3] Mingming Guo. Journal of Chemical and harmaceutical Research, 03, 5(),