RESEARCH AND SIMULATION OF THE LOCAL NAVIGATION SYSTEM OF TERRESTRIAL MOBILE ROBOT
The algorithm of complex information processing in the local navigation system of a terrestrial mobile robot and its physical model is developed. Experimental researches of this physical model have been carried out, as a result of which qualitative characteristics of the developed local navigation system have been determined. The trajectory of the object, based on the calculated navigation parameters, has a configuration identical to the actually passed route (adequate functioning of the system as a course indicator). The error in determining the coordinates of an offline object has value 0.012t2 (1.2 m per 10 s) when moving linearly and 0.022t2 (2.2 m per 10 s) when maneuvering. The orientation angles are worked out with precision (0.1÷0.3)о for roll and pitch angles and (2÷3)о for the angle of the course. Precise characteristics of the developed physical model LNS for determining orientation angles and motion parameters МR similar to the passport serial data SINS, and in some cases due to navigation features МR show even better accuracy.
local navigation system; mobile robot; algorithm of complex information processing; generalized Kalman filter; offline mode
Arvanitakis I., Giannousakis K., Tzes A.: Mobile robot navigation in unknown environment based on exploration principles. Control Applications (CCA), IEEE Conference on. IEEE, 2016, 493–498. DOI: https://doi.org/10.1109/CCA.2016.7587878
Corke P.: An introduction to inertial and vision sensing. International Journal of Robotics Research 6(26), 2007, 519–535. DOI: https://doi.org/10.1177/0278364907079279
Farrell J.A.: Aided Navigation: GPS with High Rate Sensors. McGraw-Hill, New York 2008.
Gang L., Wang J.: PRM path planning optimization algorithm research. Wseas Transactions on Systems and Control 11, 2016, 81–86.
Grewal M.S., Weill L.R., Andrews A.P.: Global Position Systems, Inertial Navigation and Integration. John Wiley & Sons, New York 2001. DOI: https://doi.org/10.1002/0471200719
Grewal M.S., Andrews A.P.: Kalman filtering: theory and practice using MATLAB. J. Wiley & Sons. Inc., New York 2001.
Groves P.D.: Principles of GNSS, Inertial and Multisensor Integrated Navigation Systems. Artech House 2008.
Ingle V.K., Proakis J.G.: Digital Signal Processing Using MATLAB. V.4. PWS Publishing Company, Boston 2009.
Ko D.W., Kim Y.N., Lee J.H., Suh I.H.: A scene-based dependable indoor navigation system. Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on. IEEE, 2016, 1530–1537. DOI: https://doi.org/10.1109/IROS.2016.7759248
Kvasnikov V.P., Rudyk A.V.: Practical estimation of errors of single-channel strapdown inertial navigation system on MEMS sensors in a short time interval. Visnyk of Ukraine Engineering Academy 1, 2017, 98–105.
Rudyk А.V.: Methods for evaluating the spatial position of objects. Integrated Intelligent Robotics (IIRTC-2016), 2016, 31–33.
Rudyk А.V.: Development of a local navigation system for a terrestrial mobile robot. Modern problems of radio electronics, telecommunications and instrumentation, Vinnytsia 2017, 75–76.
Rudyk А.V.: Comparative analysis of the accuracy characteristics of classical and accelerometric inertial navigation systems. Measurement, control and diagnostics in technical systems, Vinnytsia 2017, 209–210.
Rudyk А.V.: Analysis of the errors of MEMS accelerometers by the Allan variation method. Visnyk of Zhytomyr State Technological University. Series: Technical Sciences 1, 2017, 100–109.
Titterton D.H., Weston J.L.: Strapdown Inertial Navigation Technology. Stevenage: Institution of Electrical Engineers York, 2004.
Wang L., Zhao L., Huo G., Li R., Hou Z., Luo P., et al.: Visual semantic navigation based on deep learning for indoor mobile robots. Complexity, 2018. Article ID: 1627185. DOI: https://doi.org/10.1155/2018/1627185
Weiping Jiang, Li Wang, Xiaoji Niu, Zhang Quan, Zhang Hui, Tang Min: High-precision image aided inertial navigation with known features: observability analysis and performance evaluation. Sensors 14(10), 2014, 19371–19401. DOI: https://doi.org/10.3390/s141019371
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.