尋找特定物目標可應用於許多應用場域中，如工業自動化、倉儲物流和搜救任務。但是，在未知的環境中搜索特定物件是一個具有挑戰性的問題，因為機器人需要具備感知、探索和決策的能力，同時能夠適應動態環境。

　　過去的研究提供了各種方法和技術來解決這個問題，包括視覺感知、路徑規劃和機器學習。然而，這些方法往往受限於已知的環境或需要預先建立的地圖和物體模型。在實際應用中，可能會面對一個未知且動態的環境，所以需要一種能夠實時感知和適應環境變化的方法。

　　這項研究的目的是設計和實作一個能夠在未知環境中找到特定物體的機器人系統。該系統將整合視覺感知、決策策略和馬達控制，以達到自主尋找物體的能力。我們將使用特徵點方法進行相機圖像處理，並將其與路徑規劃演算法結合，以實現機器人在未知環境中的尋物探索。

This paper introduces a method for implementing a object-finding robot for exploration in unknown environments. Initially, the concept of a networked physical system illustrates the hardware components needed to build an autonomous mobile robot, explaining how the hardware components interact. After the hardware implementation is complete, the software focuses on two main functions, given the goal of exploring and searching in an unknown environment: "path planning algorithms for exploration in unknown environments" and "object recognition based on feature points". The article proposes three different experimental variables: "changing the field area", "altering the robot's initial facing direction", and "changing the position of obstacles". Since it's about exploring unknown environments, it's essential to use SLAM technology to record map information of the environment during its journey. Only with this step can the boundaries of visited areas be logged, directing the exploration towards other potential unknown areas for target objects.

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