本文選題：四旋翼飛行器 + 多傳感器信息融合�。� 參考：《江西理工大學(xué)》2017年碩士論文

【摘要】：隨著四旋翼飛行器在地質(zhì)勘探、農(nóng)業(yè)生產(chǎn)、物流運(yùn)輸、搶險(xiǎn)搜救等應(yīng)用領(lǐng)域的不斷深入,人們對(duì)四旋翼飛行器的要求不斷提高,達(dá)到自主控制飛行是其發(fā)展的必然趨勢(shì),而四旋翼飛行器的自主避障則是實(shí)現(xiàn)其完全自主飛行的基礎(chǔ)。此外,四旋翼飛行器應(yīng)用的普及化也帶來了許多安全問題,各種撞機(jī)、墜機(jī)事故時(shí)有發(fā)生,其對(duì)人身財(cái)產(chǎn)安全帶來了極大的威脅。因此展開對(duì)四旋翼飛行器航行中及時(shí)有效地避開障礙物以保護(hù)人身、機(jī)身和建筑物的研究具有重要意義。針對(duì)四旋翼飛行器的避障問題,文章研究了基于多傳感器信息融合與外形識(shí)別的避障算法。全文的主要內(nèi)容包括:(1)搭建了以STM32F407為主控芯片、MPU6050為慣性導(dǎo)航單元、GPS為定位導(dǎo)航系統(tǒng)的四旋翼飛行器硬件平臺(tái);采用Kalman濾波器對(duì)激光傳感器和超聲波傳感器的測(cè)距信息進(jìn)行融合,并用遺忘因子b對(duì)原算法予以優(yōu)化,然后用MATLAB中的Simulink模塊仿真得到最佳遺忘因子b值,完成了Sage_Husa自適應(yīng)Kalman濾波器的設(shè)計(jì);最后對(duì)三種材質(zhì)的障礙物進(jìn)行了距離信息融合的實(shí)驗(yàn)。(2)開展了關(guān)于障礙物外形識(shí)別的研究。介紹了基于超聲波傳感器三點(diǎn)直線排布識(shí)別障礙物外形的方法,根據(jù)其不足和局限之處,研究了一種新的測(cè)量方法——超聲波傳感器三點(diǎn)120°鈍角等腰排布測(cè)量法。針對(duì)五種基本障礙物外形,用幾何法推導(dǎo)了其測(cè)量公式,闡述了外形識(shí)別的原理,為系統(tǒng)的避障算法做好理論鋪墊。(3)基于障礙物距離信息和外形信息,制定了四旋翼飛行器的避障算法:首先,根據(jù)距離的融合信息判斷四旋翼飛行器的安全狀態(tài),以確定自動(dòng)避障系統(tǒng)的開啟與否;其次,自動(dòng)識(shí)別障礙物的外形,根據(jù)不同外形制定不同的避障措施;然后,利用PID算法實(shí)現(xiàn)四旋翼飛行器的避障動(dòng)作;最后,進(jìn)行了障礙物外形識(shí)別與避障的實(shí)驗(yàn)。多傳感器信息融合的實(shí)驗(yàn)表明:使用單一傳感器測(cè)距容易出現(xiàn)采集值不穩(wěn)定和不精確的問題,而利用Kalman濾波器進(jìn)行信息融合可以輸出穩(wěn)定的障礙物距離信息,并降低測(cè)量誤差;而Sage_Husa自適應(yīng)Kalman濾波器的融合效果在原濾波器基礎(chǔ)上進(jìn)一步得到改善。障礙物外形識(shí)別與避障的實(shí)驗(yàn)證明該避障算法可以識(shí)別出障礙物的外形,并做出相應(yīng)的避障動(dòng)作,達(dá)到避障目標(biāo)。
[Abstract]:With the deepening of the four-rotor aircraft in geological exploration, agricultural production, logistics transportation, rescue and other applications, people's requirements for the four-rotor aircraft are constantly raised, and it is the inevitable trend of its development to achieve autonomous flight control. Autonomous obstacle avoidance is the basis of complete autonomous flight of four-rotor-wing aircraft. In addition, the popularization of the application of four-rotor aircraft also brings many safety problems. Various collision and crash accidents occur from time to time, which bring great threat to personal and property safety. Therefore, it is of great significance to study the protection of human body, fuselage and buildings by avoiding obstacles in time and effectively. Aiming at the obstacle avoidance problem of four-rotor aircraft, the obstacle avoidance algorithm based on multi-sensor information fusion and shape recognition is studied in this paper. The main contents of this paper are as follows: (1) the hardware platform of a four-rotor aircraft with STM32F407 as the main control chip, MPU6050 as the inertial navigation unit and GPS as the positioning and navigation system is built, and the ranging information of laser sensor and ultrasonic sensor is fused by Kalman filter. The original algorithm is optimized by forgetting factor b, and then the optimal forgetting factor b is obtained by Simulink module in MATLAB, and the design of SageStack Husa adaptive Kalman filter is completed. Finally, the distance information fusion experiment of three kinds of obstacles is carried out. (2) the research on obstacle shape recognition is carried out. This paper introduces a method of identifying the shape of obstacle based on three point straight line arrangement of ultrasonic sensor. According to its deficiency and limitation, a new measuring method-measuring method of isosceles arrangement with three point 120 擄obtuse angle of ultrasonic sensor is studied. According to five kinds of basic obstacle shape, this paper deduces its measuring formula by geometric method, expounds the principle of shape recognition, and lays a good theoretical foundation for the obstacle avoidance algorithm of the system. (3) based on obstacle distance information and shape information, An obstacle avoidance algorithm for a four-rotor aircraft is developed. Firstly, the safety state of the four-rotor aircraft is judged according to the fusion information of the distance to determine whether the automatic obstacle avoidance system is on or not; secondly, the shape of the obstacle is automatically identified. According to different shapes, different obstacle avoidance measures are developed. Then, the four-rotor aircraft obstacle avoidance is realized by using pid algorithm. Finally, the obstacle shape recognition and obstacle avoidance experiments are carried out. The experiment of multi-sensor information fusion shows that using a single sensor to measure the distance is prone to the problem of unstable and inaccurate acquisition value, while using Kalman filter to fuse the information can output the stable obstacle distance information and reduce the measurement error. The fusion effect of SageHusa adaptive Kalman filter is further improved from the original filter. The experiments of obstacle shape recognition and obstacle avoidance show that the algorithm can recognize the shape of obstacles and make corresponding obstacle avoidance actions to achieve the goal of obstacle avoidance.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V249;TP212

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