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Research On Principle Prototype And Performance Test Of Bionic Polarization Sensor For Navigation

Posted on:2010-06-15Degree:DoctorType:Dissertation
Country:ChinaCandidate:K C ZhaoFull Text:PDF
GTID:1118360302460469Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
Navigation technology plays an important role in human survival and development. The hot issues of current research of navigation technology include: optimization of single navigation sensor performance, multiple sensors information fusion, novel principle navigation sensor exploitation. The perfect navigational capabilities of some insects afford us plenty of technology reference on novel navigation sensor research. Based on the polarization sensitivity mechanism for navigation of desert ants (Cataglyphis), a novel bionic polarization navigation sensor is designed. The work principle, function structure design, work environment character, polarization sensor performance test and the novel sensor application to outdoor mobile robot navigation are investigated systematically.A new bionic polarization navigation sensor system which mimics the polarization sensitivity mechanism of the desert ant navigation is designed. The work principle of bionic polarization sensor for navigation is discussed in detail. A novel heading angle solution method is proposed, which can overcome the zero overflows, output angle range judging method complexity and output angle function linearity difficulty. The new algorithm utilizes the high linearity degree domain completely. Compared to the algorithm in insect polarization sensitivity experiment, the output angle total error with novel algorithm can be decreased one-third with the same input error condition.The prototype machine is developed based on the work principle of polarization sensor. An embedded prototype machine based on ARM micro-controller is proposed. The hardware and software structure is presented in detail. Experiment results indicate the new bionic navigation sensor design is feasible and effective and can meet the demand of navigational engineering.The formation mechanism of atmosphere skylight polarization pattern is analysed, a whole sky polarization pattern model is created based on Rayleigh single scattering theory and celestial body motion laws. Solar position, polarization degree, polarization azimuth angle and STOKES vector of skylight are real-time calculated. Solar position's movements and skylight polarization's real-time changes are dynamic simulated. Comparing with the practical measurements the numeric results indicate the calculation model is precise and effectual.A testing instrument for polarization navigation sensor is designed and built up, which consists of photo integrating sphere and precision rotation table. Several possible sensor error causes are analyzed, a novel system error compensation method which based on least square support vector machine is proposed, and the angle error compensation model is built up. The polarization sensor character is calibrated in the dark field and uniform non-polarization light field. The maximal non-linearity angle error is less than 0.16°, and the maximal repeatability angle error with 0.10°is achieved. The angle output experiment of navigation sensor is performed in different light condition. The experiment result indicates the novel navigation sensor is with high precision and robust.A new head reckoning navigation system integrating polarization sensor and odometer (optic-electric code plate) is proposed with extended Kalman filter. Compared with the traditional reckoning navigation system combining gyro (or magnetic compass) and odometer, the novel navigation system has several merits: low cost, non system error float and little interference with electric-magnetic environment. The outdoor mobile robot path track experiments show the new polarization sensor is with simple work principle, high precision and robust performance.
Keywords/Search Tags:Navigation Sensor, Polarization Vision, Performance Test, Error Compensation Algorithm, Least Square Support Vector Machine, Kalman Filter
PDF Full Text Request
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