慣性元件所量測之加速度訊號經過積分處理可以得到速度，位移等運動狀態相關資訊，其運動狀態之量測對於迴授控制與定位導航領域皆為不可或缺之需求。但是以往通常需要使用高等級之慣性元件與測速儀來達到一定程度之訊號準確度，而隨著微機電製程技術的進步，微型慣性元件目前已經有一定程度的發展，且具有體積小、低功耗與製造成本低等優勢；但是因為存在訊號飄移的問題，以至於獨立應用於導航定位方面與速度量測方面的精確度明顯不足，本研究主要目的為探討訊號飄移形成之原因，接著經由不同的訊號處理方式如: 防飄移積分器、訊號特徵識別法、卡曼濾波器、零速更新法與位置更新法經過整合來抑制微型加速規之訊號飄移誤差；本文藉由雙鉗樑振動速度量測實驗與線性馬達定位實驗來驗證本研究所提出之訊號飄移抑制法之抑制飄移誤差能力，同時以全向輪定位實驗來探討以微型慣性元件對於機器載具進行室內定位之可行性。

In guidance, navigation, and control applications, precisely determination of velocity and position are essential for proper system operations. Those information can be obtained by integrating measured acceleration signals. Traditionally, using high grade inertial sensors to achieve the accuracy is the exclusive method but the cost may not be affordable for non-military or non-research applications. In this dissertation, the author presents an alternative way by using low-cost, small-sized solid state inertia sensors to replace those high-priced sensors. These low cost inertia sensors suffer from severe signal drafting after integration, which must be suppressed in order to maintain the functionality. In this thesis, the compositions of the inertia sensor outputs are analyzed first. Then, several anti-drafting mechanisms are proposed and examined; including anti-drifting integrator, heuristic signal identification filters, Kalman filters, and state update methods. The capabilities and limitations on suppressing signal drifting of the proposed methods are then evaluated by two test beds: the fixed-fixed beam system is used to examine the possibility of velocity reconstruction using accelerometers, while the linear motor dring stage is used for exploring indoor inertia navigation. The results indicate that the proposed schemes can successfully suppress the signal drifting. In particular, the scheme containing Kalman filter could achieve the best results. Finally, the proposed methods are demonstrated by using a small mobile omni-robots. The experiment result shows the mean error is 0.03m in 3.5m travel distance and 47 seconds duration and the results indicate that the proposed method could be useful in indoor positioning for smart living applications.

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