Jordan Henrio

称号及び氏名博士（工学）

学位授与の日付

2019 年 3 月 31 日

論

Development of a Surveillance System using AutonomousDrones

文

名(自律ドローンを用いた監視システムの開発)

論文審査委員

主査

中島 智晴

副査

吉岡理文

副査

藤本典幸

論文要旨

Recent advances in robotics and artificial intelligence have allowed the development of cutting-edgetechnologies. These new techniques opened new horizons for societies, reflected by the emergence ofnew terms such as internet of things, big data, autonomous vehicles, home robots, smart cities, smart
farms and so forth. These terms correspond to the combination of several technologies employed toautomate cumbersome tasks that require high-level expertise or cognitive processes.
Unmanned Aerial Vehicles (UAVs), that are also popularly named drones due to the sound they
produce during flying, play a major role in the conception of smart environments because of theirubiquitous applicability. Their aerial point of view offers a global perspective of a given area and allows
the consideration of new approaches for various applications. Examples of drone applications include
crop fields mapping or monitoring, wildfire monitoring, animal census, cellular communication and soforth. The ability of drones to fly at different altitudes and their relatively small size makes them aninteresting tool for building inspection. In addition, they are a promising tool to perform tasks that
represent a danger for humans, such as air pollution, radiation measurements, and post-disastermonitoring.
A smart city generally refers to a combination of various tools employed for the automation of city
infrastructures such as shops, delivery, traffic management, waste management and so forth.
Surveillance is a frequent theme when designing smart cities. This task refers to monitoring a given
environment via different sensors (e.g., cameras) in order to detect abnormal or hazardous situations.
Currently, surveillance agents have to carefully watch continuous video streams for several hours todetect or trying to prevent dangerous situations. Therefore, it is currently a laborious responsibility. Inaddition to this difficulty, current surveillance systems generally rely on fixed-position cameras. Suchdevices could represent a limitation to the task in cases requiring target tracking. Drones embedding acamera represent a promising alternative (or complement) to fixed-position cameras.
This dissertation presents a possible approach to the development of an autonomous surveillancesystem employing drones as moving cameras. The goal is to ease the cumbersome aspects ofsurveillance by sending drones to check situations at particular checkpoints, and raising alerts when anabnormal situation is detected. This work focuses on the two popular topics encountered in systems
involving drones; namely, navigation and data processing. It is noteworthy that despite security is an
important concern in any systems, this subject is out of the scope of this work.
In the proposed system, a human supervisor selects, through a graphical user interface, a set ofcheckpoints to visit. This set of checkpoints is processed by a route planning module which searches
the best visiting order and allocates checkpoints among drones. Next, the module sends a plan to thedifferent drones. Then, drones visit their designated checkpoints and continuously send recorded videos
to a data processing module. This one looks for anomalies in received data. When an anomaly isdetected, it sends an alert to the human supervisor. The two different modules used in this system arefurther explained below.
The route planning module tackles task allocation problems by using the A* algorithm to determine
the path between any two points. Then, it employs a discrete firefly algorithm to optimize thepermutation of checkpoints. The order of checkpoints is optimized according to an objective functionthat considers not only the distance traveled by the teams of robots but also the degree of uncertainty
about checkpoints' situations. We hypothesize that as long as optimization progresses, checkpoints
close together in the environment tend to be grouped in the permutation as well. Therefore, the discrete
firefly algorithm investigated in this essay performs local exploration on a neighborhood of contiguous
elements in the permutation. In addition, the size of the neighborhood is decreased as iterations increasein order to limit exploration to mix groups of checkpoints.
Meta-heuristic algorithms, such as the firefly algorithm, have to face with the trade-off between
exploration and exploitation. This trade-off is generally managed by the different algorithm's hyperparameters. However, tuning such an algorithm is generally a laborious step and requires
meticulousness. Grid-search and random search approaches are commonly employed for this task.
However, these approaches lack of strategy and blindly try out combinations. Bayesian optimization isa recently proposed method that infers the optimal combination of hyper-parameters by modeling theoutcome landscape of an algorithm from prior trials. In this dissertation, we employ such a method tooptimize the performance of our discrete firefly algorithm.
The data processing module deals with anomaly detection in videos recorded by drones. There arenumerous domains such as surveillance, medicine or industry where anomalies should be detected. The
concept of anomaly is ill-defined and highly depends on the problem

審査結果の要旨
本論文は，自律ドローンを用いた敷地内の安全を見守る巡回システムを構築するという目標のもと，大規模なシステムを構築するための課題を同定し，その課題を解決するためにソフトコンピューティングや機械学習を提案する研究であり，以下の成果を確認した．
（１） 巡回システムを構築するためには，複数の自律ドローンが巡回するルート計画の自動作成方法の開発と，ドローンが撮影した動画像からの自動以上検出が課題であることを論じ，課題を同定した．また，自律ドローンが巡回するルートを自動決定する課題を制約付き数理最適化問題として定式化した．定式化では，敷地内にドローンのチェックポイントを設定し，チェックポイントの安全レベルとその経時変化モデルを設定したうえで，ドローンのバッテリー消費量を最小化し，かつ安全レベルの最大化を達成する巡回ルートを求める問題と定めることに成功した．
（２） （１）で定式化したルート自動決定問題に対して，メタヒューリスティックスの適用を提案し，最適なルートを決定することに成功した．メタヒューリスティックスとして蛍アルゴリズムを提案した．蛍アルゴリズムは従来，実数値最適化のために提案されたアルゴリズムであるが，これを順列最適化問題にもできるように，離散蛍アルゴリズムを提案した．また，さらなる性能向上を目指して蛍を表す順列表現にセグメントの概念を導入し，探索範囲を調整して最適解探索性能を効率化することが可能であることを明らかにした．
（３） ドローンの積載カメラによって撮影された敷地風景に対して，敷地内の異常を自動で検知する人工知能的手法を開発した．機械学習アプローチを適用して動画像の異常検知モデルを構築するために，教師あり学習に基づくモデル学習手法を提案した．異常検知モデルとして，動画像を入力とする深層学習モデルを提案した．異常検知課題においては，モデル学習に用いるラベル付き動画像を準備する必要があるが，以上ラベルがついた動画像が利用不可能であることが多い．そのため，教師あり学習モデルについて異常検知の文脈から実験的考察を行い，実現可能性が低いことを示した．
（４） （３）の結果を受け，教師無し学習や半教師付き学習の枠組みを使って異常検知モデルを構築する手法を提案した．モデル学習のために利用するデータには正常もしくは異常のラベルを付けず，回帰問題として異常検知モデル学習を定式化した．異常検知モデルとして自己回帰型 PixelCNN を提案し，動画像内の隣接ピクセルの確率的生成モデルを学習することに成功した．
以上の諸成果は，ソフトコンピューティングや機械学習の実問題における有用性の向上に大いに貢献すると期待され，本分野の学術的・産業的な発展に貢献するところ大である．また，申請者が自立して研究活動を行うのに必要な能力と学識を有することを証したものである．