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Wang // Northwestern Polytechnic University Press. 2012. 386 p.89.145Qin, Y. Q. et al. Path planning for mobile robot using the particle swarmoptimization with mutation operator / Y. Q. Qin, D. B. Sun, N. Li // Machine Learningand Cybernetics, 2004. Proceedings of 2004 International Conference on. IEEE, 2004.Vol. 4. P.
2473–2478.90.Qu, L. et al. An SLAM algorithm based on improved UKF / L. Qu, S. He,Y. Qu // Control and Decision Conference (CCDC), 2012 24th Chinese. IEEE, 2012.P. 4154–4157.91.Rasmussen, C. et al. Probabilistic data association methods for trackingcomplex visual objects / C. Rasmussen, G. D. Hager // Pattern Analysis and MachineIntelligence, IEEE Transactions on. 2001. № 23(6). P. 569–576.92.Roberts, A. et al.
Adaptive Position Tracking of VTOL UAVs / A. Roberts, A.Tayebi // IEEE. Trans. on Rob. 2011. № 27. P. 129-142.93.Roh, H. C. et al. Fast SLAM using polar scan matching and particle weightbased occupancy grid map for mobile robot / H. C. Roh, C. H. Sung, M.
T. Kang //Ubiquitous Robots and Ambient Intelligence (URAI). 2011 8th International Conferenceon. IEEE, 2011. P. 756–757.94.Santos, O. et al. Real-time Stabilization of a Quadrotor UAV: NonlinearOptimal and Suboptimal Control / O. Santos, H. Romero, S. Salazar, R. Lozano // J. IntellRobot Syst. 2013. Vol. 70. P. 79–91.95.Segars, A.
H. et al. Re-examining perceived ease of use and usefulness: Aconfirmatory factor analysis / A. H. Segars, V. Grover // MIS quarterly. 1993.P. 517–525.96.Senba, A. et al. Transient response analysis with extended Kalman filter forrecursive self-identification using semi-active devices / A. Senba, H.
Furuya // CD-Proc.48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and MaterialConference. AIAA, 2007. P. 1–8.97.Smith, S. M. et al. SUSAN — a new approach to low level image processing/ S. M. Smith, J. M. Brady // International journal of computer vision. 1997. № 23(1). P.45–78.98.146Sola, J. et al. Impact of landmark parametrization on monocular EKF-SLAMwith points and lines / J.
Sola, T. Vidal-Calleja, J. Civera // International Journal ofComputer Vision. 2012. № 97(3). P. 339–368.99.Sun, F. et al. A data association method based on simulate anneal arithmeticfor mobile robot SLAM / F. Sun, T. Wang, W. Lu // Automation and Logistics. IEEEInternational Conference on. IEEE, 2008. P.
425–430.100. Takeshi, A. Motion planning for multiple obstacles avoidance of autonomousmobile robot using hierarchical fuzzy rule / A. Takeshi // Proceedings of IEEEInternational Conference on Multi-sensor Fusion and Integration for Intelligent System(MFI’94).
Las Vegas, 1994. P. 265–271.101. Tkachev, S. B. et al. Design of Path Following Method for Unmanned AerialVehicles using Normal Forms / S. B. Tkachev, W. Liu // IFAC-Papers OnLine. 2015.№ 48 (11). P. 10–15.102. Tong, L. et al. Passive and active nonlinear fault tolerant control of aquadrotor unmanned aerial vehicle based on the sliding mode control technique / L. Tong,Z. Youmin, Gordon B.
W. // Journal of Systems and Control Engineering. 2013. Vol. 227.P. 12–23.103. Tu, Y. et al. The Mobile Robot SLAM Based on Depth and Visual Sensing inStructured Environment / Y. Tu, Z. Huang, X. Zhang // Robot Intelligence Technologyand Applications. 2015. P. 343–357.104. Upadhyay, T. N. et al. Autonomous GPS/INS navigation experiment forspace transfer vehicle / T.
N. Upadhyay, S. Cotterill, A. W. Deaton // IEEE Transactionson Aerospace and Electronic Systems. 1993. № 29(3). P. 772–785.105. Upadhyay, T. N. et al. Autonomous reconfigurable GPS/INS navigation andpointing system forrendezvous and docking / T. Upadhyay, S. Cotterill, A. Deaton // SpacePrograms and Technologies Conference. 1991. P. 1390.106.
Wan, E. et al. The unscented Kalman filter for nonlinear estimation / E. Wan,R. Van der Merwe // Adaptive Systems for Signal Processing, Communications, andControl Symposium, 2000. AS-SPCC. IEEE, 2000. P. 153–158.147107. Welzl, E. Constructing the visibility graph for n-line segments in O (n 2) time/ E. Welzl // Information Processing Letters. 1985. № 20(4). P.
167–171.108. Xian, B. et al. Nonlinear robust output feedback tracking control of aquadrotor UAV using quaternion representation / B. Xian, C. Diao, B. Zhao // NonlinearDynamics. 2014. P. 1–18.109. Xiong, Z. et al. Fuzzy adaptive Kalman filter for marine INS/GPS navigation/ Z. Xiong, Y. Hao, J. Wei // Mechatronics and Automation, 2005 IEEE InternationalConference. IEEE, 2005. № 2. P.
747–751.110. Yang, S. X. et al. An efficient neural network approach to dynamic robotmotion planning / S. X. Yang, M. Meng // Neural Networks. 2000. № 13(2). P. 143–148.111. Yang, S. X. et al. Neural network approaches to dynamic collision-freetrajectory generation / S. X.
Yang, M. Meng // Systems, Man, and Cybernetics, Part B:Cybernetics, IEEE Transactions on. 2001. № 31(3). P. 302–318.112. Ye-Lun, X. et al. Flight theory in atmospheric disturbance / X. Ye-Lun,J. Chang-Jiang // Beijing: National Defence Industry Press, 1993. P. 73–74.113. Yi, Z. et al. Tracking control for UAV trajectory / Z. Yi, Y. Xiuxia, Z. Hewei //Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese. IEEE,2014. P. 1889–1894.114.
Yost, D. J. et al. Command to line-of-sight guidance: A stochastic optimalcontrol problem / D. J. Yost, J. E. Kain // Journal of Spacecraft. 1977. № 14(7). P.438–444.115. Yu, J. et al. Fault Detection Using Propagator for Kalman Filter and ItsApplication to SDINS / J. Yu, J. G. Lee, C. G. Park // ICCAS. 2003. P. 978–983.116. Yuan, G. et al. A Variable Proportion Adaptive Federal Kalman Filter forINS/ESGM/GPS/DVL Integrated Navigation System / G.
Yuan, K. Yuan, H. A. Zhang //Computational Sciences and Optimization (CSO), 2011 Fourth International JointConference on. IEEE, 2011. P. 978–981.117. Yue, Bai et al. Trajectory tracking control of a quad-rotor UAV based oncommand filtered backstepping / Yue Bai, Cheng Peng, Changjun Zhao, Yantao Tian //1482012 Third International Conference on Intelligent Control and Information Processing.IEEE, 2012. P. 179–184.118. Zarchan, P. Tactical and Strategic Missile Guidance, volume 176 of Progressin Astronautics and Aeronautics / P. Zarchan // AIAA, third edition, 1997. P.
95–118.119. Zhang, S. et al. An efficient data association approach to simultaneouslocalization and map building / S. Zhang, L. Xie, M. Adams // The International Journalof Robotics Research. 2005. № 24(1). P. 49–60.120. Zhu, Q. B. et al. An ant colony algorithm based on grid method for mobilerobot path planning / Q.
B. Zhu, Y. Zhang // Robot. 2005. № 27(2). P. 132–136.121. Zuo, Z. Trajectory tracking control design with command-filteredcompensation for a quadrotor / Z. Zuo // Control Theory & Applications, IET. 2010. №4(11). P. 2343–2355.122. Zhang T. et al. A new method of seamless land navigation for GPS/INSintegrated system / T. Zhang, X. Xu // Measurement. 2012. № 45(4).
P. 691–701.123. Zhang J. et al. Shoe-mounted personal navigation system based on MEMSinertial technology / J. Zhang, Y. Qin, C. B. Mei // Journal of Chinese Inertial Technology.2011. № 19(3). P. 253–256.149ПриложениеП. 1. Программы Matlab алгоритма облѐта препятствийfunction xx=avoiding_obstacles(u)Cx=u(1);Cy=u(2);Cz=u(3);Vx=u(4);Vy=u(5);Vz=u(6);t_delta=u(7);d_dan=5; x_g=100;y_g=38.5;z_g=17; k1=3;k2=5;distance=sqrt((Cx-x_g)^2+(Cy-y_g)^2+(Cz-z_g)^2);[Rx,Ry,Rz,CCx,CCy,CCz]=obstacles;hh=patch(0,0,0,'r','erasemode','xor');hhh=patch(0,0,0,'b','erasemode','xor');D_R=[75;10;20];m_vx=0.2;m_vy=0.8;m_vz=0.01;m_acx=-0.1;m_acy=0.1; m_acz=0.01;N=size(Rx,1);ac=[0,0,0];XX=Cx;YY=Cy;ZZ=Cz;q=0.01; r=0.01; Q=q^2*eye(7); R=r^2;f=@(x)[x(1)+x(4)*t_delta+0.5*m_acx*t_delta^2;x(2)+x(5)*t_delta+0.5*m_acy*t_delta^2;x(3)+x(6)*t_delta+0.5*m_acz*t_delta^2;x(4)+m_acx*t_delta;x(5)+m_acy*t_delta;x(6)+m_acz*t_delta;x(7)];hh=@(x) [sqrt((x(1)-Cx)^2+(x(2)-Cy)^2+(x(3)-Cz)^2);2*asin(x(5)/sqrt((x(1)-Cx)^2+(x(2)-Cy)^2)+(x(3)-Cz)^2);acos(((x(1)-Cx)*Vx+(x(2)-Cy)*Vy+(x(3)-Cz)*Vz)...(sqrt((x(1)-Cx)^2+(x(2)-Cy)^2+(x(3)-Cz)^2)*sqrt(Vx^2+Vy^2+Vz^2)))];s=[75;10;20;2;8;0.1;5];xxx=s+q*randn(7,1);P = eye(7);tt=0;xV =[];sV=[];XV=[];k=0;dt=t_delta;MX=[];MY=[];MZ=[];DRX=[];DRY=[];DRZ=[];h=patch();while distance>=5MX=[MX,m_acx];MY=[MY,m_acy];MY=[MY,m_acy];m_acx=m_acx+0.001*randn(1);m_acy=m_acy+0.001*randn(1);m_acz=m_acz+0.001*randn(1);DRX=[DRX,D_R(1)]; DRY=[DRY,D_R(2)];DRZ=[DRZ,D_R(3)];[D_RR,AV,m_vx,m_vy,m_acx,m_acy]=d_obstacles(t_delta,D_R,m_vx,m_vy,m_vz,m_acx,m_acy,m_acz,h);C_R=collision(Cx,Cy,Cz,ac,Vx,Vy,Vz,AV,D_R,h);dis=dis_obstacles(Cx,Cy,Rx,CCx,CCy);distance=sqrt((Cx-x_g)^2+(Cy-y_g)^2+(Cz-z_g)^2);150vec=Vector(Cx,Cy,CCx,CCy,Rx);[Ac,ac_obstacles,ac_goal]=acceleration(Cx,Cy,Cz,x_g,y_g,z_g,Vx,Vy,Vz,V,distance,Rx,vec,dis,d_dan,k1,k2,CCx,CCy);ac=Ac;Cx=Cx+Vx*t_delta+0.5*ac(1)*t_delta^2;Cy=Cy+Vy*t_delta+0.5*ac(2)*t_delta^2;Cz=Cz+Vz*t_delta+0.5*(ac(3))*t_delta^2;Vx=Vx+ac(1)*t_delta;Vy=Vy+ac(2)*t_delta;Vz=Vz+ac(3)*t_delta;k=k+1;sV(:,k)=[tt;s]; XV1(:,k)=[Cx;Cy;Cz];xx=ac;end151П.