因工業4.0的興起，促使自動化產線與無人工廠蓬勃的發展。而這些自動化工廠常常需要自主式(Autonomous)的無人搬運車(Automatic guided vehicle)來輔助，以維持產線運作。然其工作環境可能狹窄且侷限。而常見的阿克曼機構(Ackermann steering geometry)的無人搬運車轉彎時具有最小迴轉半徑(Radius of turn)之要求，所以此型之無人搬運車的車體尺寸要求以及使用環境限制較多。另一方面，全向車(Omni-directional vehicle)在移動上之機動性與靈活度非常高，具全向性且可隨時向任何方向移動，屬於完整系統(Holonomic system)，也無最小迴轉半徑的限制，在移動上極具優勢。全向車系統其應用領域廣大，例如輪椅、活動床等生活領域，或是無人搬運車、機械手臂結合等工業領域，具有極高的發展與應用價值。然而，自主式的系統最令人擔憂的是潛在之突如其來的故障或失效。其後果可能造成金錢上的損失，甚至影響人員的安危。
本研究探討使用常見的特殊輪，麥克納姆輪(Mecanum wheel)的全向車。全向車具備全向性皆因麥克納姆輪的特殊結構，所以若其車輪產生故障，很可能導致全向車失去全向性。故本研究探討兩個針對麥克納姆輪全向車的車輪故障檢測(Wheel fault detection)之方法，分別使用訊號式與模型式之方法進行故障檢測。研究探討的車輪故障預設案例為麥克納姆輪上之小輪被異物卡住導致小輪無法轉動之故障。其中訊號式方法利用加速度感測器測量車體振動，在利用快速傅立葉轉換(Fast Fourier transform)對訊號進行分析，再設立故障指標與閥值進行故障檢測。而模型式方法則利用全向車馬達的輸入與輸出，以具有遺忘因子(Forgetting factor)之遞迴最小平方法(Recursive least square)進行參數鑑別，再利用鑑別之參數變化進行故障檢測。透過模擬與實驗證實兩種方法的假設與可行性，並比較其優缺點。

Driven by Industry 4.0, automated production lines and unmanned factories have drawn much attention and thus flourished in many applications. These automated factories often require autonomous vehicles to operate production lines. The operating environment for the vehicle is usually narrow and confined. On the other hand, the common Ackermann steering geometry automatic guided vehicle have a minimum radius of turning. Therefore, there are limitations on using Ackermann automatic guided vehicle such as its size and space restriction. In contrast, omni-directional vehicles have high mobility and flexibility and are omnidirectional to move in any direction at any time. They are holonomic system with zero radius of gyration. The omni-directional vehicle system has a wide range of applications, such as wheelchairs, automatic guided vehicles form house-hold application to industrial application. Thus, theresearch and development in autonomous omni-directional vehicles has extremely high value in many aspects. However, the most concerning issue regarding to the opearion of an autonomous system is its reliability and the potential consequence of its unexpected failures, which may cause financial losses and even safety hazzards.
In this research, the Mecanum wheel coustructed omnidirectional vehicle is under consideration. Due to Mecanum wheel’s roller arrangement, the vehicle is omni-directional. However, this special feature is degredated or lossed if the wheel fails. Therefore, two fault detection methods, signal-based and model-based, for Mecanum wheel omni-directional vehicles are investigated for wheel fault. These two methods are applied to Mecanum wheel fault simulated for the situation that the rollers are stucked due to a foreign object. An acceleration sensor is used to measure vehicle body vibration for signal-based method. The recorded vibration date is further analized using Fast Fourier Transform to explore its frequency content and then associated fault indicators are observed and corresponding thresholds established for fault detection. The model-based method uses the input and output data of the omni-directional vehicle motor to identify associated parameters by using the Recursive Least Square method with a forgetting factor. Those identified system parameters are then used as indicators for fault detection. The capability and performance of these two methods under considered Mecanum wheel fault are compared in simulation and with experiment result.

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