Learning-based reconnaissance mission planning for communication-aware unmanned aerial vehicle system통신 인지형 무인기 시스템의 학습 기반 정찰 임무 계획

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dc.contributor.advisorAhn, Jaemyung-
dc.contributor.authorChoi, Uihwan-
dc.description학위논문(박사) - 한국과학기술원 : 항공우주공학과, 2021.2,[iv, 100 p. :]-
dc.description.abstractThis thesis formulates a multi-target reconnaissance mission planning problem for UAV system with air-to-ground and air-to-air communication models. Then, a machine learning-based planning framework is proposed to obtain a near-optimal planning solution with fast runtime. The problem focuses on mission level planning, such as path planning and task planning under communication requirements during the mission period. Conventional optimization approaches for UAV mission planning problems have suffered from their high computational complexity, one of the main obstacles in developing near-optimal online planning algorithms. As an alternative, adequately trained deep neural networks for control/planning problems have been reported as fast and near-optimal online controllers/planners even with low onboard computation resources. This thesis aims to develop a learning-based framework for the communication-aware UAV mission planning problem. This thesis covers both single-UAV system with air-to-ground communication model and multi-UAV system with air-to-air/ground communication model. For a single-UAV system, this thesis proposes Markov decision process (MDP) formulation for a problem to plan the flight path and task sequence for reconnaissance of multiple targets under risk of communication loss to a ground base. A framework for training dataset generation, offline learning, and online planning is proposed. Various air-to-air communication models are considered for the multi-UAV system, including multi-hop communication, network connectivity constraint, and dynamic topology change. Then multi-UAV network mission planning problem is suggested to include issues on multi-UAV task assignment and multi-UAV communication network design problem. Both multi-agent reinforcement learning approach and optimization-based approach are proposed. Case studies on multi-target reconnaissance using single/multi-UAV have been conducted to demonstrate the effectiveness of the proposed approaches for mission environments under communication restrictions.-
dc.subjectCommunication-aware UAV mission planning▼aMulti-UAV task planning▼aCommunication network desgin▼aMDP▼aMINLP▼aImitation learning▼aMulti-agent reinforcement learning-
dc.subject통신 인지형 무인기 임무 계획▼a다수 무인기 작업 계획▼a무선 통신 네트워크 설계▼a마르코프 의사결정 과정▼a혼합 정수 비선형 계획법▼a모방학습▼a다개체 강화학습-
dc.titleLearning-based reconnaissance mission planning for communication-aware unmanned aerial vehicle system-
dc.title.alternative통신 인지형 무인기 시스템의 학습 기반 정찰 임무 계획-
dc.description.department한국과학기술원 :항공우주공학과,-
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