根據國際消防培訓協會(International Fire Service Training Association, IFSTA)的訓練手冊[1]，對於搜尋室內火災受災者的原則是讓消防員沿著牆壁並依序搜尋的初步搜尋(Primary search)。初步搜尋可幫助消防員避免在陌生環境中迷路，並確保沒有遺漏任何受災者。然而，這可能會在沒有受災者的房間裡浪費時間，從而增加受災者的傷亡和消防員的危險。由於無人駕駛車的發展，機器人現在被用於火災中。在本研究中，機器人用於收集室內火災資訊，包括受災者的位置和危險區域，以加快搜尋速度並減少火災中的損失。本研究設計了一種加快搜救機器人搜尋速度的方法。透過基於群眾外包(Crowdsourcing)和環境資訊(Environmental information)估計受災者的位置和狀態來確定搜尋順序。搜救機器人採用最佳路徑規劃演算法執行該搜尋順序。本研究在模擬實驗中得到了驗證，實驗結果表明我們的方法提供了具有競爭力的表現，特別是在顯著減少受災者的平均搜尋時間方面。與初步搜尋相比，我們的方法將受災者的平均搜尋時間減少了 25%。

According to IFSTA [1], the principle of searching victims in indoor fire is primary search which is sequential search along with the walls. Primary search assists firefighters to avoid being lost in unfamiliar environment and ensure no victim omitted. However, it possibly wastes time on rooms without victims so that increases casualty of victims and danger of firefighters. Owing to the development of unmanned vehicles, robots are now utilized in fires. In this paper, robots are used to collect indoor fire information including the locations of victims and the dangerous area to speed up search and decrease damages in fires. This study designs an approach for speeding up search of rescue robots. It determines the search sequence by estimating the locations and status of victims based on crowdsourced and environmental information. The rescue robot adopts optimal path planning algorithm to execute the search sequence. This study is validated in simulated experiments, and the experimental results demonstrate that our approach provides competitive performance, especially in significantly decreasing average search time of a victim. Compared to sequential search, our approach reduces the average search time of a victim by 25%.

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