近年來，空中機器人被廣泛地應用於不同領域中，像是在交通不便的地區搜索以及救援、軍事探勘、目標追蹤等。傳統的海洋救災應變需要大量的人力資源、船隻，以及時間，船舶自動辨識系統 (AIS) 藉由船舶交通管理系統 (VTS)能夠用於自動識別與定位海洋船舶以防止意外發生。然而，現今的船舶自動辨識系統侷限於僅能架設基地台於陸地上，使得船舶自動辨識系統掃瞄範圍受到限制，為了克服這些限制，本論文提出使用空中機器人結合自動船舶辨識系統，藉由控制站以及發生事故的船隻所回傳之GPS信號導引空中機器人飛行至指定高度或是事故現場，待空中機器人飛行至指定位置時擷取裝載於救生衣上之AIS訊號發送器並即時回傳至控制站使救災過程更順利並有效縮短救援搜索時間。
本論文所提出之實際應用方法在完成救援任務後，空中機器人得自主降落於所設移動降落目標上，本文利用垂直起降式空中機器人將影像處理以及視覺導引演算法以及影像伺服控制追蹤降落目標。此外，藉由卡爾曼濾波器以及粒子濾波器過濾相關感測器資料以便更精準地量測降落目標物之位置，藉由從這些方法空中機器人得以較小的誤差降落在移動降落平台上。

Aerial mobile robots are widely used in various applications such as search and rescue in inaccessible areas, military expeditions, and object tracking. Traditional marine disaster rescue response requires lots of human sources, marine ships, and time. An Automatic Identification System (AIS) is an automatic tracking system used on ships and by Vessel Traffic Services (VTS) for identifying and locating vessels to prevent the accidents. However, current AIS stations are based on land and limited due to being stationary with a short scan range. In order to overcome these limitations, an aerial mobile robot integrated with AIS is proposed in this thesis. The AMR will be guided by the control station and fly to the accident site through GPS signal launched by AIS on the accident vessel. Simultaneously, it will collect the AIS signal which is mounted on the emergency vest and transmit it back to the control station to make the rescue process smoother and more efficient.
This thesis proposes a practical method of control for AMR autonomous landing on moving targets after executing the rescue mission. This research is focused on applying vision navigation and image processing algorithms throughout vertical-takeoff and landing (VTOL) AMR as well as image-based visual servoing (IBVS) to track the landing target. Further, we improve our results by applying the Kalman filter and Particle filter over relevant sensor data to more accurately measure the target's location and the UAV's own location. By applying these methods, we are able to land on a moving landing target within smaller error as measured from the UAV.

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