In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time. This is an extension of the well-known multiple traveling salesman problem. The proposed formulation can be applied in several domains such as the monitoring of traffic flows in a transportation network, or the monitoring of remote locations to assist search and rescue missions. Aiming to find the optimal schedule, the problem is formulated as an Integer Linear Program (ILP). Given that the problem is highly combinatorial, the optimal solution scales only for small sized problems. Thus, a greedy algorithm is also proposed that uses a one-step look ahead heuristic search mechanism. In a detailed evaluation, it is observed that the greedy algorithm has near-optimal performance as it is on average at 92.06% of the optimal, while it can potentially scale up to settings with hundreds of drones and locations.
A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone
Université Catholique de Louvain（鲁汶大学）；Hanoi University of Technology（河内理工大学）；University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学）
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Suleyman Demirel University（苏莱曼·德米雷尔大学）；UNIST（蔚山科学技术院）；Korea University（高丽大学）；Amazon（亚马逊）；University of South Florida（南佛罗里达大学）
强化学习最近在许多组合最优化问题的学习质量解决方案中显示出了希望。特别是，基于注意力的编码器-解码器模型在各种路由问题上表现出很高的效率，包括流动推销员问题（TSP）。不幸的是，它们在TSP with Drone（TSP-D）中表现不佳，需要协调不同的车辆-卡车和无人机。在TSP-D中，两辆车一前一后地移动，并且可能需要在节点处等待另一辆车加入。基于注意力的无状态解码器无法在车辆之间进行这种协调。我们提出了一个混合模型，它使用注意编码器和长短期记忆（LSTM）网络解码器，其中解码器的隐藏状态可以表示所做的动作序列。我们的经验表明，这样的混合模型提高了一个纯粹的基于注意力的模型的解决方案的质量和计算效率。最小-最大容量限制车辆路径问题（mmCVRP）的实验结果也表明，混合模型比基于注意力的模型更适合于多车辆的协调路径问题。所提出的模型展示了与运筹学基线方法相当的结果。
Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs
MIT Media Lab（麻省理工学院媒体实验室）；Transportation Research Institute, Technion（以色列理工学院交通研究院）；Department of Civil Engineering, Technion（以色列理工学院土木工程系）；Computer Science Department, Technion（以色列理工学院计算机科学系）
An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones
School of Management Science and Real Estate, Chongqing University（重庆大学管理科学与房地产学院）；School of Management, Guizhou University（贵州大学管理学院）；Chongqing Changan Minsheng APLL Logistics Co., Ltd.（重庆长安民生APLL物流有限公司）；State Grid Chongqing Electric Power Company Material Branch（国网重庆市电力公司物资分公司）
Distributed Model Predictive Control with Adaptive Safety Zones for Multi-Fleet Drone Operations
Institute of Automation and Computer Control（自动化与计算机控制研究所）；Ruhr University Bochum（博德姆鲁尔大学）；Department of Logistics and Supply Chain Management（物流与供应链管理系）；South Westphalia University of Applied Sciences（西南弗劳恩霍夫应用科学大学）
Autonomous Unmanned Aircraft Systems for Enhanced Search and Rescue of Drowning Swimmers: Image-Based Localization and Mission Simulation
Chair of Engineering Mathematics and Numerical Optimization（工程数学与数值优化系）；Brandenburg University of Technology Cottbus–Senftenberg（克滕-萨森堡技术大学）；Chair of Applied Mathematics（应用数学系）
