两足行走机器人—行走结构部分设计【带三维】【优秀含16张CAD图纸+全套机械毕业设计】两足行走机器人—行走结构部分设计【带三维】【优秀含16张CAD图纸+全套机械毕业设计】

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折叠 两足行走机器人—行走结构部分设计【优秀含16张CAD图纸+全套机械毕业设计】.zip两足行走机器人—行走结构部分设计【优秀含16张CAD图纸+全套机械毕业设计】.zip
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Intelligent Control of an Autonomous Mobile Robot using Type-2 Fuzzy Logic Leslie Astudillo, Oscar Castillo, Patricia Melin, Arnulfo Alanis, Jose Soria, Luis T. Aguilar Abstract We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a kinematic controller and a torque controller based on Fuzzy Logic Theory. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems. Index TermsIntelligent Control, Type-2 Fuzzy Logic, Mobile Robots. I. INTRODUCTION Mobile robots are nonholonomic systems due to the constraints imposed on their kinematics. The equations describing the constraints cannot be integrated simbolically to obtain explicit relationships between robot positions in local and global coordinate’s frames. Hence, control problems involve them have attracted attention in the control community in the last years [11]. Different methods have been applied to solve motion control problems. Kanayama et al. [10] propose a stable tracking control method for a nonholonomic vehicle using a Lyapunov function. Lee et al. [12] solved tracking control using backstepping and in [13] with saturation constraints. Furthermore, most reported designs rely on intelligent control approaches such as Fuzzy Logic Control [1][8][14][17][18][20] and Neural Networks [6][19]. However the majority of the publications mentioned above, has concentrated on kinematics models of mobile robots, which are controlled by the velocity input, while less attention has been paid to the control problems of nonholonomic dynamic systems, where forces and torques are the true inputs Bloch Manuscript received December 15, 2005 qnd accepted on April 5, 2006. This work was supported in part by the Research Council of DGEST under Grant 493.05-P. The students also were supported by CONACYT with scholarships for their graduate studies. Oscar Castillo is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico corresponding author phone 52664-623-6318; fax 52664-623-6318; e-mail ocastillotectijuana.mx. Patricia Melin is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail hariastectijuana.mx. Arnulfo Alanis is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail pmelintectijuana.mx Leslie Astudillo is a graduate student in Computer Science with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail pmelintectijuana.mx Jose Soria is a with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail jsoriaucsd.edu. Luis Aguilar is with CITEDI-IPN Tijuana, Mexico e-mail laguilarcitedi.mx and Drakunov [2] and Chwa [4], used a sliding mode control to the tracking control problem. Fierro and Lewis [5] propose a dynamical extension that makes possible the integration of kinematic and torque controller for a nonholonomic mobile robot. Fukao et al. [7], introduced an adaptive tracking controller for the dynamic model of mobile robot with unknown parameters using backstepping. In this paper we present a tracking controller for the dynamic model of a unicycle mobile robot, using a control law such that the mobile robot velocities reach the given velocity inputs, and a fuzzy logic controller such that provided the required torques for the actual mobile robot. The rest of this paper is organized as follows. Sections II and III describe the formulation problem, which include the kinematic and dynamic model of the unicycle mobile robot and introduces the tracking controller. Section IV illustrates the simulation results using the tracking controller. The section V gives the conclusions. II. PROBLEM FORMULATION A. The Mobile Robot The model considered is a unicycle mobile robot see Fig. 1, it consist of two driving wheels mounted on the same axis and a front free wheel [3]. Fig. 1. Wheeled mobile robot. The motion can be described with equation 1 of movement in a plane [5] Engineering Letters, 132, EL_13_2_7 Advance online publication 4 August 2006______________________________________________________________________________________ wvq100sin0cosθθ is the vector of velocities, v and w are the linear and angular velocities respectively; is the input vector, is a symmetric and positive-definite inertia matrix, is the centripetal and Coriolis matrix, is the gravitational vector. Equation 1.a represents the kinematics or steering system of a mobile robot. Notice that the no-slip condition imposed a non-holonomic constraint described by 2, that it means that the mobile robot can only move in the direction normal to the axis of the driving wheels. Twvv ],[rR∈τnxnRqM ∈nxnRqqV ∈, nRqG ∈0sincos − θθ xy2 B. Tracking Controller of Mobile Robot Our control objective is established as follows Given a desired trajectory qdt and orientation of mobile robot we must design a controller that apply adequate torque τ such that the measured positions qt achieve the desired reference qdt represented as 3 0lim −∞→tqtqdt3 To reach the control objective, we are based in the procedure of [5], we deriving a τt of a specific vct that controls the steering system 1.a using a Fuzzy Logic Controller FLC. A general structure of tracking control system is presented in the Fig. 2. III. CONTROL OF THE KINEMATIC MODEL We are based on the procedure proposed by Kanayama et al. [10] and Nelson et al. [15] to solve the tracking problem for the kinematic model, this is denoted as vct. Suppose the desired trajectory qdsatisfies 4 dddddwvq100sin0cosθθ 4 Using the robot local frame the moving coordinate system x-y in figure 1, the error coordinates can be defined as 5 θθθθθθθ−−−−−dddyxdeyyxxeeeqqTe1000cossin0sincos, 5 And the auxiliary velocity control input that achieves tracking for 1.a is given by 6 θθekvekvwekevwvvefvdyddxdccdccsincos,,321 6 Where k1, k2and k3are positive constants. IV. FUZZY LOGIC CONTROLLER The purpose of the Fuzzy Logic Controller FLC is to find a control input τ such that the current velocity vector v to reach the velocity vector vcthis is denoted as 7 0lim −∞→vvct7 As is shown in Fig. 2, basically the FLC have 2 inputs variables corresponding the velocity errors obtained of 7 denoted as ev and ew linear and angular velocity errors respectively, and 2 outputs variables, the driving and rotational input torques τ denoted by F and N respectively. The membership functions MF[9] are defined by 1 triangular and 2 trapezoidal functions for each variable involved due to the fact are easy to implement computationally. Fig. 3 and Fig. 4 depicts the MFs in which N, C, P represent the fuzzy sets [9] Negative, Zero and Positive respectively associated to each input and output variable, where the universe of discourse is normalized into [-1,1] range. Fig. 2. Tracking control structure Fig. 3. Membership function of the input variables evand ew Fig. 4. Membership functions of the output variables F and N. The rule set of FLC contain 9 rules which governing the input-output relationship of the FLC and this adopts the Mamdani-style inference engine [16], and we use the center of gravity method to realize defuzzification procedure. In Table I, we present the rule set whose format is established as follows Rule i If evis G1and ewis G2then F is G3and N is G4 Where G1..G4are the fuzzy set associated to each variable and i 1 ... 9. TABLE 1 FUZZY RULE SET In Table I, N means NEGATIVE, P means POSITIVE and C means ZERO. V. SIMULATION RESULTS Simulations have been done in Matlab® to test the tracking controller of the mobile robot defined in 1. We consider the initial position q0 0, 0, 0 and initial velocity v0 0,0. From Fig. 5 to Fig. 8 we show the results of the simulation for the case 1. Position and orientation errors are depicted in the Fig. 5 and Fig. 6 respectively, as can be observed the errors are sufficient close to zero, the trajectory tracked see Fig. 7 is very close to the desired, and the velocity errors shown in Fig. 8 decrease to zero, achieving the control objective in less than 1 second of the whole simulation. We show in Fig. 9 the Simulink block diagram to test the controller. We also show in Fig. 10 the tracking errors in the three variables. Finally, we show in Fig. 11 the evolution of the genetic algorithm that was used to find the optimal parameters for the fuzzy controller. Fig. 5. Positions error with respect to the reference values. Solid error in x, dotted error in y. Fig. 6. Orientation error with respect to the reference values. Fig. 7. Mobile Robot Trajectory. Fig. 8. Velocity errors Solid error in e , dotted error in ev w Fig. 9 Simulink block diagram of the controller. Fig. 10 Tracking errors in the three variables. Fig. 11 Evolution of GA for finding optimal Controller. In Table II we show simulation results for 25 experiments with different conditions for the gains of the fuzzy controller. We can also appreciate from this table that different reference velocities and positions were considered. TABLE II SIMULATION RESULTS FOR DIFFERENT EXPERIMENTS WITH THE FUZZY CONTROLLER. VI. CONCLUSIONS We described the development of a tracking controller integrating a fuzzy logic controller for a unicycle mobile robot with known dynamics, which can be applied for both, point stabilization and trajectory tracking. Computer simulation results confirm that the controller can achieve our objective. As future work, several extensions can be made to the control structure of Fig. 2, such as to increase the tracking accuracy and the performance level. REFERENCES [1] S. Bentalba, A. El Hajjaji, A. Rachid, Fuzzy Control of a Mobile Robot A New Approach, Proc. IEEE Int. Conf. On Control Applications, Hartford, CT, pp 69-72, October 1997. [2] A. M. Bloch, S. Drakunov, Tracking in NonHolonomic Dynamic System Via Sliding Modes, Proc. IEEE Conf. On Decision Control, Brighton, UK, pp 1127-1132, 1991. [3] G. Campion, G. Bastin, B. D’Andrea-Novel, Structural Properties and Classification of Kinematic and Dynamic Models of Wheeled Mobile Robots, IEEE Trans. On Robotics and Automation, Vol. 12, No. 1, February 1996. [4] D. Chwa., Sliding-Mode Tracking Control of Nonholonomic Wheeled Mobile Robots in Polar coordinates, IEEE Trans. On Control Syst. Tech. Vol. 12, No. 4, pp 633-644, July 2004. [5] R. Fierro and F.L. Lewis, Control of a Nonholonomic Mobile Robot Backstepping Kinematics into Dynamics. Proc. 34th Conf. on Decision Control, New Orleans, LA, 1995. [6] R. Fierro, F.L. Lewis, Control of a Nonholonomic Mobile Robot Using Neural Networks, IEEE Trans. On Neural Networks, Vol. 9, No. 4, pp 589 – 600, July 1998. [7] T. Fukao, H. Nakagawa, N. Adachi, Adaptive Tracking Control of a NonHolonomic Mobile Robot, IEEE Trans. On Robotics and Automation, Vol. 16, No. 5, pp. 609-615, October 2000. [8] S. Ishikawa, A Method of Indoor Mobile Robot Navigation by Fuzzy Control, Proc. Int. Conf. Intell. Robot. Syst., Osaka, Japan, pp 1013-1018, 1991. [9] J. S. R. Jang, C.T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Sadle River, NJ, 1997. [10] Y. Kanayama, Y. Kimura, F. Miyazaki T. Noguchi, A Stable Tracking Control Method For a Non-Holonomic Mobile Robot, Proc. IEEE/RSJ Int. Workshop on Intelligent Robots and Systems, Osaka, Japan, pp 1236- 1241, 1991. [11] I. Kolmanovsky, N. H. McClamroch., Developments in Nonholonomic Nontrol Problems, IEEE Control Syst. Mag., Vol. 15, pp. 20–36, December. 1995. [12] T-C Lee, C. H. Lee, C-C Teng, Tracking Control of Mobile Robots Using the Backsteeping Technique, Proc. 5th. Int. Conf. Contr., Automat., Robot. Vision, Singapore, pp 1715-1719, December 1998. [13] T-C Lee, K. Tai, Tracking Control of Unicycle-Modeled Mobile robots Using a Saturation Feedback Controller, IEEE Trans. On Control Systems Technology, Vol. 9, No. 2, pp 305-318, March 2001. [14] T. H. Lee, F. H. F. Leung, P. K. S. Tam, Position Control for Wheeled Mobile Robot Using a Fuzzy Controller, IEEE pp 525-528, 1999. [15] W. Nelson, I. Cox, Local Path Control for an Autonomous Vehicle, Proc. IEEE Conf. On Robotics and Automation, pp. 1504-1510, 1988. [16] K. M. Passino, S. Yurkovich, “Fuzzy Control”, Addison Wesley Longman, USA 1998. [17] S. Pawlowski, P. Dutkiewicz, K. Kozlowski, W. Wroblewski, Fuzzy Logic Implementation in Mobile Robot Control, 2nd Workshop On Robot Motion and Control, pp 65-70, October 2001. [18] C-C Tsai, H-H Lin, C-C Lin, Trajectory Tracking Control of a Laser-Guided Wheeled Mobile Robot, Proc. IEEE Int. Conf. On Control Applications, Taipei, Taiwan, pp 1055-1059, September 2004. [19] K. T. Song, L. H. Sheen, Heuristic fuzzy-neural Network and its application to reactive navigation of a mobile robot, Fuzzy Sets Systems, Vol. 110, No. 3, pp 331-340, 2000. [20] S. V. Ulyanov, S. Watanabe, V. S. Ulyanov, K. Yamafuji, L. V. Litvintseva, G. G. Rizzotto, Soft Computing for the Intelligent Robust Control of a Robotic Unicycle with a New Physical Measure for Mechanical Controllability, Soft Computing 2 pp 73 – 88, Springer- Verlag, 1998. Oscar Castillo is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, he is serving as Research Director of Computer Science and head of the research group on fuzzy logic and genetic algorithms. Currently, he is President of HAFSA Hispanic American Fuzzy Systems Association and Vice-President of IFSA International Fuzzy Systems Association in charge of publicity. Prof. Castillo is also Vice-Chair of the Mexican Chapter of the Computational Intelligence Society IEEE. Prof. Castillo is also General Chair of the IFSA 2007 World Congress to be held in Cancun, Mexico. He also belongs to the Technical Committee on Fuzzy Systems of IEEE and to the Task Force on “Extensions to Type-1 Fuzzy Systems”. His research interests are in Type-2 Fuzzy Logic, Intuitionistic Fuzzy Logic, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. He has published over 50 journal papers, 5 authored books, 10 edited books, and 160 papers in conference proceedings. Patricia Melin is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, she is serving as Director of Graduate Studies in Computer Science and head of the research group on fuzzy logic and neural networks. Currently, she is Vice President of HAFSA Hispanic American Fuzzy Systems Association and Program Chair of International Conference FNG’05. Prof. Melin is also Chair of the Mexican Chapter of the Computational Intelligence Society IEEE. She is also Program Chair of the IFSA 2007 World Congress to be held in Cancun, Mexico. She also belongs to the Committee of Women in Computational Intelligence of the IEEE and to the New York Academy of Sciences. Her research interests are in Type-2 Fuzzy Logic, Modular Neural Networks, Pattern Recognition, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. She has published over 50 journal papers, 5 authored books, 8 edited books, and 140 papers in conference proceedings. Leslie Astudillo is a graduate student in Computer Science with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. She has published 2 papers in Conference Proceedings. Arnulfo Alanis is a Professor with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. He has published 2 Journal papers and 15 Conference Proceedings papers. Jose Soria is a Professor with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. He has published 4 Journal papers and 5 Conference Proceedings papers. Luis Aguilar is a Professor with the Center for Research in Digital Systems in Tijuana, Mexico. He has published 5 Journal papers and 15 Conference Proceedings papers. He is member of the National System of Researchers of Mexico, and member of IEEE. He is member of the IEEE Computational Intelligence-Chapter Mexico, and member of the Hispanic American Fuzzy Systems Association. He is also member of the International Program Committees of several Conferences, and reviewers of several International Journals.

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Intelligent Control of an Autonomous Mobile Robot using Type-2 Fuzzy Logic Leslie Astudillo, Oscar Castillo, Patricia Melin, Arnulfo Alanis, Jose Soria, Luis T. Aguilar Abstract We develop a tracking controller for the dynamic model of unicycle mobile robot by integrating a kinematic controller and a torque controller based on Fuzzy Logic Theory. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems. Index TermsIntelligent Control, Type-2 Fuzzy Logic, Mobile Robots. I. INTRODUCTION Mobile robots are nonholonomic systems due to the constraints imposed on their kinematics. The equations describing the constraints cannot be integrated simbolically to obtain explicit relationships between robot positions in local and global coordinate’s frames. Hence, control problems involve them have attracted attention in the control community in the last years [11]. Different methods have been applied to solve motion control problems. Kanayama et al. [10] propose a stable tracking control method for a nonholonomic vehicle using a Lyapunov function. Lee et al. [12] solved tracking control using backstepping and in [13] with saturation constraints. Furthermore, most reported designs rely on intelligent control approaches such as Fuzzy Logic Control [1][8][14][17][18][20] and Neural Networks [6][19]. However the majority of the publications mentioned above, has concentrated on kinematics models of mobile robots, which are controlled by the velocity input, while less attention has been paid to the control problems of nonholonomic dynamic systems, where forces and torques are the true inputs Bloch Manuscript received December 15, 2005 qnd accepted on April 5, 2006. This work was supported in part by the Research Council of DGEST under Grant 493.05-P. The students also were supported by CONACYT with scholarships for their graduate studies. Oscar Castillo is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico corresponding author phone 52664-623-6318; fax 52664-623-6318; e-mail ocastillotectijuana.mx. Patricia Melin is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail hariastectijuana.mx. Arnulfo Alanis is with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail pmelintectijuana.mx Leslie Astudillo is a graduate student in Computer Science with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail pmelintectijuana.mx Jose Soria is a with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico e-mail jsoriaucsd.edu. Luis Aguilar is with CITEDI-IPN Tijuana, Mexico e-mail laguilarcitedi.mx and Drakunov [2] and Chwa [4], used a sliding mode control to the tracking control problem. Fierro and Lewis [5] propose a dynamical extension that makes possible the integration of kinematic and torque controller for a nonholonomic mobile robot. Fukao et al. [7], introduced an adaptive tracking controller for the dynamic model of mobile robot with unknown parameters using backstepping. In this paper we present a tracking controller for the dynamic model of a unicycle mobile robot, using a control law such that the mobile robot velocities reach the given velocity inputs, and a fuzzy logic controller such that provided the required torques for the actual mobile robot. The rest of this paper is organized as follows. Sections II and III describe the formulation problem, which include the kinematic and dynamic model of the unicycle mobile robot and introduces the tracking controller. Section IV illustrates the simulation results using the tracking controller. The section V gives the conclusions. II. PROBLEM FORMULATION A. The Mobile Robot The model considered is a unicycle mobile robot see Fig. 1, it consist of two driving wheels mounted on the same axis and a front free wheel [3]. Fig. 1. Wheeled mobile robot. The motion can be described with equation 1 of movement in a plane [5] Engineering Letters, 132, EL_13_2_7 Advance online publication 4 August 2006______________________________________________________________________________________ wvq100sin0cosθθ is the vector of velocities, v and w are the linear and angular velocities respectively; is the input vector, is a symmetric and positive-definite inertia matrix, is the centripetal and Coriolis matrix, is the gravitational vector. Equation 1.a represents the kinematics or steering system of a mobile robot. Notice that the no-slip condition imposed a non-holonomic constraint described by 2, that it means that the mobile robot can only move in the direction normal to the axis of the driving wheels. Twvv ],[rR∈τnxnRqM ∈nxnRqqV ∈, nRqG ∈0sincos − θθ xy2 B. Tracking Controller of Mobile Robot Our control objective is established as follows Given a desired trajectory qdt and orientation of mobile robot we must design a controller that apply adequate torque τ such that the measured positions qt achieve the desired reference qdt represented as 3 0lim −∞→tqtqdt3 To reach the control objective, we are based in the procedure of [5], we deriving a τt of a specific vct that controls the steering system 1.a using a Fuzzy Logic Controller FLC. A general structure of tracking control system is presented in the Fig. 2. III. CONTROL OF THE KINEMATIC MODEL We are based on the procedure proposed by Kanayama et al. [10] and Nelson et al. [15] to solve the tracking problem for the kinematic model, this is denoted as vct. Suppose the desired trajectory qdsatisfies 4 dddddwvq100sin0cosθθ 4 Using the robot local frame the moving coordinate system x-y in figure 1, the error coordinates can be defined as 5 θθθθθθθ−−−−−dddyxdeyyxxeeeqqTe1000cossin0sincos, 5 And the auxiliary velocity control input that achieves tracking for 1.a is given by 6 θθekvekvwekevwvvefvdyddxdccdccsincos,,321 6 Where k1, k2and k3are positive constants. IV. FUZZY LOGIC CONTROLLER The purpose of the Fuzzy Logic Controller FLC is to find a control input τ such that the current velocity vector v to reach the velocity vector vcthis is denoted as 7 0lim −∞→vvct7 As is shown in Fig. 2, basically the FLC have 2 inputs variables corresponding the velocity errors obtained of 7 denoted as ev and ew linear and angular velocity errors respectively, and 2 outputs variables, the driving and rotational input torques τ denoted by F and N respectively. The membership functions MF[9] are defined by 1 triangular and 2 trapezoidal functions for each variable involved due to the fact are easy to implement computationally. Fig. 3 and Fig. 4 depicts the MFs in which N, C, P represent the fuzzy sets [9] Negative, Zero and Positive respectively associated to each input and output variable, where the universe of discourse is normalized into [-1,1] range. Fig. 2. Tracking control structure Fig. 3. Membership function of the input variables evand ew Fig. 4. Membership functions of the output variables F and N. The rule set of FLC contain 9 rules which governing the input-output relationship of the FLC and this adopts the Mamdani-style inference engine [16], and we use the center of gravity method to realize defuzzification procedure. In Table I, we present the rule set whose format is established as follows Rule i If evis G1and ewis G2then F is G3and N is G4 Where G1..G4are the fuzzy set associated to each variable and i 1 ... 9. TABLE 1 FUZZY RULE SET In Table I, N means NEGATIVE, P means POSITIVE and C means ZERO. V. SIMULATION RESULTS Simulations have been done in Matlab® to test the tracking controller of the mobile robot defined in 1. We consider the initial position q0 0, 0, 0 and initial velocity v0 0,0. From Fig. 5 to Fig. 8 we show the results of the simulation for the case 1. Position and orientation errors are depicted in the Fig. 5 and Fig. 6 respectively, as can be observed the errors are sufficient close to zero, the trajectory tracked see Fig. 7 is very close to the desired, and the velocity errors shown in Fig. 8 decrease to zero, achieving the control objective in less than 1 second of the whole simulation. We show in Fig. 9 the Simulink block diagram to test the controller. We also show in Fig. 10 the tracking errors in the three variables. Finally, we show in Fig. 11 the evolution of the genetic algorithm that was used to find the optimal parameters for the fuzzy controller. Fig. 5. Positions error with respect to the reference values. Solid error in x, dotted error in y. Fig. 6. Orientation error with respect to the reference values. Fig. 7. Mobile Robot Trajectory. Fig. 8. Velocity errors Solid error in e , dotted error in ev w Fig. 9 Simulink block diagram of the controller. Fig. 10 Tracking errors in the three variables. Fig. 11 Evolution of GA for finding optimal Controller. In Table II we show simulation results for 25 experiments with different conditions for the gains of the fuzzy controller. We can also appreciate from this table that different reference velocities and positions were considered. TABLE II SIMULATION RESULTS FOR DIFFERENT EXPERIMENTS WITH THE FUZZY CONTROLLER. VI. CONCLUSIONS We described the development of a tracking controller integrating a fuzzy logic controller for a unicycle mobile robot with known dynamics, which can be applied for both, point stabilization and trajectory tracking. Computer simulation results confirm that the controller can achieve our objective. As future work, several extensions can be made to the control structure of Fig. 2, such as to increase the tracking accuracy and the performance level. REFERENCES [1] S. Bentalba, A. El Hajjaji, A. Rachid, Fuzzy Control of a Mobile Robot A New Approach, Proc. IEEE Int. Conf. On Control Applications, Hartford, CT, pp 69-72, October 1997. [2] A. M. Bloch, S. Drakunov, Tracking in NonHolonomic Dynamic System Via Sliding Modes, Proc. IEEE Conf. On Decision Control, Brighton, UK, pp 1127-1132, 1991. [3] G. Campion, G. Bastin, B. D’Andrea-Novel, Structural Properties and Classification of Kinematic and Dynamic Models of Wheeled Mobile Robots, IEEE Trans. On Robotics and Automation, Vol. 12, No. 1, February 1996. [4] D. Chwa., Sliding-Mode Tracking Control of Nonholonomic Wheeled Mobile Robots in Polar coordinates, IEEE Trans. On Control Syst. Tech. Vol. 12, No. 4, pp 633-644, July 2004. [5] R. Fierro and F.L. Lewis, Control of a Nonholonomic Mobile Robot Backstepping Kinematics into Dynamics. Proc. 34th Conf. on Decision Control, New Orleans, LA, 1995. [6] R. Fierro, F.L. Lewis, Control of a Nonholonomic Mobile Robot Using Neural Networks, IEEE Trans. On Neural Networks, Vol. 9, No. 4, pp 589 – 600, July 1998. [7] T. Fukao, H. Nakagawa, N. Adachi, Adaptive Tracking Control of a NonHolonomic Mobile Robot, IEEE Trans. On Robotics and Automation, Vol. 16, No. 5, pp. 609-615, October 2000. [8] S. Ishikawa, A Method of Indoor Mobile Robot Navigation by Fuzzy Control, Proc. Int. Conf. Intell. Robot. Syst., Osaka, Japan, pp 1013-1018, 1991. [9] J. S. R. Jang, C.T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing A Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Sadle River, NJ, 1997. [10] Y. Kanayama, Y. Kimura, F. Miyazaki T. Noguchi, A Stable Tracking Control Method For a Non-Holonomic Mobile Robot, Proc. IEEE/RSJ Int. Workshop on Intelligent Robots and Systems, Osaka, Japan, pp 1236- 1241, 1991. [11] I. Kolmanovsky, N. H. McClamroch., Developments in Nonholonomic Nontrol Problems, IEEE Control Syst. Mag., Vol. 15, pp. 20–36, December. 1995. [12] T-C Lee, C. H. Lee, C-C Teng, Tracking Control of Mobile Robots Using the Backsteeping Technique, Proc. 5th. Int. Conf. Contr., Automat., Robot. Vision, Singapore, pp 1715-1719, December 1998. [13] T-C Lee, K. Tai, Tracking Control of Unicycle-Modeled Mobile robots Using a Saturation Feedback Controller, IEEE Trans. On Control Systems Technology, Vol. 9, No. 2, pp 305-318, March 2001. [14] T. H. Lee, F. H. F. Leung, P. K. S. Tam, Position Control for Wheeled Mobile Robot Using a Fuzzy Controller, IEEE pp 525-528, 1999. [15] W. Nelson, I. Cox, Local Path Control for an Autonomous Vehicle, Proc. IEEE Conf. On Robotics and Automation, pp. 1504-1510, 1988. [16] K. M. Passino, S. Yurkovich, “Fuzzy Control”, Addison Wesley Longman, USA 1998. [17] S. Pawlowski, P. Dutkiewicz, K. Kozlowski, W. Wroblewski, Fuzzy Logic Implementation in Mobile Robot Control, 2nd Workshop On Robot Motion and Control, pp 65-70, October 2001. [18] C-C Tsai, H-H Lin, C-C Lin, Trajectory Tracking Control of a Laser-Guided Wheeled Mobile Robot, Proc. IEEE Int. Conf. On Control Applications, Taipei, Taiwan, pp 1055-1059, September 2004. [19] K. T. Song, L. H. Sheen, Heuristic fuzzy-neural Network and its application to reactive navigation of a mobile robot, Fuzzy Sets Systems, Vol. 110, No. 3, pp 331-340, 2000. [20] S. V. Ulyanov, S. Watanabe, V. S. Ulyanov, K. Yamafuji, L. V. Litvintseva, G. G. Rizzotto, Soft Computing for the Intelligent Robust Control of a Robotic Unicycle with a New Physical Measure for Mechanical Controllability, Soft Computing 2 pp 73 – 88, Springer- Verlag, 1998. Oscar Castillo is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, he is serving as Research Director of Computer Science and head of the research group on fuzzy logic and genetic algorithms. Currently, he is President of HAFSA Hispanic American Fuzzy Systems Association and Vice-President of IFSA International Fuzzy Systems Association in charge of publicity. Prof. Castillo is also Vice-Chair of the Mexican Chapter of the Computational Intelligence Society IEEE. Prof. Castillo is also General Chair of the IFSA 2007 World Congress to be held in Cancun, Mexico. He also belongs to the Technical Committee on Fuzzy Systems of IEEE and to the Task Force on “Extensions to Type-1 Fuzzy Systems”. His research interests are in Type-2 Fuzzy Logic, Intuitionistic Fuzzy Logic, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. He has published over 50 journal papers, 5 authored books, 10 edited books, and 160 papers in conference proceedings. Patricia Melin is a Professor of Computer Science in the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico. In addition, she is serving as Director of Graduate Studies in Computer Science and head of the research group on fuzzy logic and neural networks. Currently, she is Vice President of HAFSA Hispanic American Fuzzy Systems Association and Program Chair of International Conference FNG’05. Prof. Melin is also Chair of the Mexican Chapter of the Computational Intelligence Society IEEE. She is also Program Chair of the IFSA 2007 World Congress to be held in Cancun, Mexico. She also belongs to the Committee of Women in Computational Intelligence of the IEEE and to the New York Academy of Sciences. Her research interests are in Type-2 Fuzzy Logic, Modular Neural Networks, Pattern Recognition, Fuzzy Control, Neuro-Fuzzy and Genetic-Fuzzy hybrid approaches. She has published over 50 journal papers, 5 authored books, 8 edited books, and 140 papers in conference proceedings. Leslie Astudillo is a graduate student in Computer Science with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. She has published 2 papers in Conference Proceedings. Arnulfo Alanis is a Professor with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. He has published 2 Journal papers and 15 Conference Proceedings papers. Jose Soria is a Professor with the Division of Graduate Studies and Research in Tijuana Institute of Technology, Mexico. He has published 4 Journal papers and 5 Conference Proceedings papers. Luis Aguilar is a Professor with the Center for Research in Digital Systems in Tijuana, Mexico. He has published 5 Journal papers and 15 Conference Proceedings papers. He is member of the National System of Researchers of Mexico, and member of IEEE. He is member of the IEEE Computational Intelligence-Chapter Mexico, and member of the Hispanic American Fuzzy Systems Association. He is also member of the International Program Committees of several Conferences, and reviewers of several International Journals.
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两足行走机器人——行走结构部分设计.doc

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毕业答辩.ppt

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双足行走机器人头部和身体.dwg

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大腿舵机连接件.dwg

头.dwg

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手臂舵机连接件.dwg

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身体连接件.dwg

机器人三维设计图


毕业设计说明书(论文)中文摘要


20世纪40年代,伴随着遥控操纵器和数控制造技术的出现,关于机器人技术的研究开始出现。经过几十年的发展,数百种不同结构、不同控制系统、不同用途的机器人已进入了实用化阶段。目前,机器人大多以轮子的形式实现行走功能阶段。真正模仿人类用腿走路的机器人还不多,虽有一些六足、四足机器人涌现,但是两足机器人还是凤毛麟角。本课题主要针对两足机器人的行走进行研究、分析、设计,然后材料加工制作,最后进行组装和行走调试。



关键词  研究  设计  制作


毕业设计说明书(论文)外文摘要


Title          The Robot Move with Two-legs                  


             — Designing of Stepped  Structure                                                


Abstract

In 1940s, along with the remote control and digital manufacturing technologies, the researches on robot technology were appeared. After decades of development, hundreds of different structures, different control systems, and different uses of the robots have been entered into a practical stage. At present, most robots use   wheel to realize the function phase of walking. But there is little robot can imitate human walking that with two legs , although a number of six-legged, four-legged robots have emerged, two-legged robots are rare. So the main topics of this  study is to research, analysis and design on the two-legged walking robot, and product materials, then set up them and do some adjustment through walking in the final.


Keywords  research  design  facture

目   录


1  绪论1

1.1  引言1

1.2  机器人的发展及技术1

1.3  两足机器人的优点及国内外研究概况2

1.4  本课题的主要工作7

2  双足机器人本体结构设计分析8

2.1  引言8

2.2  两足机器人的结构分析8

2.3  机器人设计思路9

2.4  机器人设计方案10

2.5  驱动方式的选择12

3  双足机器人的具体制作13

3.1  双足机器人的材料选择13

3.2  双足机器人的零件加工13

3.3  两足机器人的组装16

3.4  两足机器人相关数据19

3.5  两足机器人总体尺寸19

3.6  舵机具体参数19

4  课题总结20

结束语21

致谢22

参考文献23



1  绪论

1.1  引言

目前,机器人已形成一个不同技术层次、应用于多种环境的“庞大”家族,从天上到地下,从陆地到海洋到处都可以看到机器人的身影。世界著名机器人专家,日本早稻田大学的加藤一郎教授曾经指出“机器人应当具有的最大的特征之一是步行功能”。步行机器人的研究涉及到多门学科的交叉融合,如仿生学、机构学、控制理论与工程学、电子工程学、计算机科学及传感器信息融合等。仿人形机器人正成为机器人研究中的一个热点,其研究水平,在一定程度上代表了一个国家的高科技发展水平和综合实力。研究仿人形双足步行机器人,除了具有重要的学术意义,还有现实的应用价值。

参 考 文 献


[1]  周远清,张再兴等编著. 智能机器人系统[M]. 北京: 清华大学出版社,1989.

[2]  蒋新松主编. 机器人学导论[M]. 沈阳: 辽宁科学技术出版社,1994.

[3]  方建军,何广平. 智能机器人[M]. 北京:化学工业出版社,2004.

[4]  张永学. 双足机器人步态规划及步行控制研究[D]. 哈尔滨工业大学博士学位论文. 2001.

[5]  刘志远. 两足机器人动态行走研究[D]. 哈尔滨工业大学博士论文. 1991.

[6]  刘志远,戴绍安,裴润,张栓,傅佩深. 零力矩点与两足机器人动态行走稳定性的关系[N]. 哈尔滨工业大学学报. 1994.

[7]  纪军红. HIT-Ⅱ双足步行机器人步态规划研究[D]. 哈尔滨工业大学博士论文,2000.

[8]  麻亮,纪军红,强文义,傅佩深. 基于力矩传感器的双足机器人在线模糊步态调整器设计[R]. 2000.

[9]  竺长安. 两足步行机器人系统分析、设计及运动控制[D]. 国防科技大学博士论文. 1992.

[10] 马宏绪. 两足步行机器人动态步行研究[D]. 国防科技大学博士论文. 1995.

[11] 包志军. 仿人型机器人运动特性研究[D]. 上海交通大学博士论文. 2000.

[12] 孙富春,朱纪洪,刘国栋等. 机器人学导论-分析、系统及应用[M]. 北京:电子工业出版社,2004.

[13] 柳洪义,宋伟刚. 机器人技术基础[M]. 北京:冶金工业出版社,2002

[14] 刘晋春,白基成,郭永丰. 特种加工[M]. 北京:机械工业出版社,2008.3.

[15] 解仑,王志良,李华俊.双足步行机器人制作技术[M]. 北京:机械工业出版社,2008.4.


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