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routeview:an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data

International Journal of Digital Earth 2022年第1期15卷 1588-1613页

作者： Adan Wu Tao Che Xin Li Xiaowen Zhu Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research StationNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhouPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China CAS Center for Excellence in Tibetan Plateau Earth Sciences Beijing China Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijingPeople’s Republic of China Gansu Forestry Polytechnic TianshuiPeople’s Republic of China

The potential of Arctic routes(ARs)has attracted global attention,and exploiting the Arctic has become an important strategy for many countries.However,there are still some challenges for ships sailing in Arctic ice zones,including harsh marine environments and the insufficient service capacity of sea ice information service systems.To better understand the route changes in the Arctic and extract real-time ship navigation routes,we developed an online interactive route planning system(routeView)for ships sailing in the Arctic based on big Earth data.routeView includes two main features:(1)an online calculation interface is provided for optimal routes along the Arctic Northeast Passage(NEP)60 days into the future by utilizing reinforcement learning(RL)based on sea ice and meteorological data,and(2)an online ice-water classification is established based on synthetic aperture radar(SAR)data and deep learning to help users extract the sea ice distribution in real time.This work can potentially enhance the safety of shipping navigation along the NEP and improve information extraction methods for ARs.

关键词： Arctic northeast passage information extraction system reinforcement learning U-Net route planning ice-water classification

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Robust global route planning for an autonomous underwater vehicle in a stochastic environment

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Frontiers of Information Technology & Electronic Engineering 2022年第11期23卷 1658-1672页

作者： Jiaxin ZHANG Meiqin LIU Senlin ZHANG Ronghao ZHENG State Key Laboratory of Industrial Control Technology Zhejiang UniversityHangzhou 310027China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong UniversityXi'an 710049China

This paper describes a route planner that enables an autonomous underwater vehicle to selectively complete part of the predetermined tasks in the operating ocean area when the local path cost is stochastic.The problem is formulated as a variant of the orienteering problem.Based on the genetic algorithm(GA),we propose the greedy strategy based GA(GGA)which includes a novel rebirth operator that maps infeasible individuals into the feasible solution space during evolution to improve the efficiency of the optimization,and use a differential evolution planner for providing the deterministic local path cost.The uncertainty of the local path cost comes from unpredictable obstacles,measurement error,and trajectory tracking error.To improve the robustness of the planner in an uncertain environment,a sampling strategy for path evaluation is designed,and the cost of a certain route is obtained by multiple sampling from the probability density functions of local paths.Monte Carlo simulations are used to verify the superiority and effectiveness of the planner.The promising simulation results show that the proposed GGA outperforms its counterparts by 4.7%–24.6%in terms of total profit,and the sampling-based GGA route planner(S-GGARP)improves the average profit by 5.5%compared to the GGA route planner(GGARP).

关键词： Autonomous underwater vehicle route planning Genetic algorithm Orienteering problem Stochastic path cost

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Energy Aware Data Collection with route planning for 6G Enabled UAV Communication

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Computers, Materials & Continua 2022年第4期71卷 825-842页

作者： Mesfer Al Duhayyim Marwa Obayya Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammed Rizwanullah Majdy M.Eltahir Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

With technological advancements in 6G and Internet of Things(IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellularnetworks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timelysolutions for real-time applications such as medicine, tracking, surveillance,etc. Energy efficiency, data collection, and route planning are crucial processesto improve the network communication. These processes are highly difficultowing to high mobility, presence of non-stationary links, dynamic topology,and energy-restricted UAVs. With this motivation, the current research paperpresents a novel Energy Aware Data Collection with Routing planning for6G-enabled UAV communication (EADCRP-6G) technique. The goal of theproposed EADCRP-6G technique is to conduct energy-efficient cluster-baseddata collection and optimal route planning for 6G-enabled UAV networks.EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) andorganize these clusters. Besides, Artificial Fish Swarm-based route planning(AFSRP) technique is applied to choose an optimum set of routes for UAVcommunication in 6G networks. In order to validated whether the proposedEADCRP-6G technique enhances the performance, a series of simulationswas performed and the outcomes were investigated under different dimensions.The experimental results showcase that the proposed model outperformed allother existing models under different evaluation parameters.

关键词： Unmanned aerial vehicle 6G networks artificial intelligence energy efficiency clustering route planning data collection metaheuristics

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Integrated route planning and Resource Allocation for Connected Vehicles

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China Communications 2021年第3期18卷 226-239页

作者： Quan Yuan Bo Chen Guiyang Luo Jinglin Li Fangchun Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Laboratory of High-end Server and Storage Technology Jinan 250101ShandongChina

Intelligent and connected vehicles have leveraged edge computing paradigm to enhance their environment comprehension and behavior planning capabilities.As the quantity of intelligent vehicles and the demand for edge computing are increasing rapidly,it becomes critical to efficiently orchestrate the communication and computation resources on edge clouds.Existing methods usually perform resource allocation in a fairly effective but still reactive manner,which is subject to the capacity of nearby edge clouds.To deal with the contradiction between the spatiotemporally varying demands for edge computing and the fixed edge cloud capacity,we proactively balance the edge computing demands across edge clouds by appropriate route planning.In this paper,route planning and resource allocation are jointly optimized to enhance intelligent driving.We propose a multi-scale decentralized optimization method to deal with the curse of dimensionality.In large-scale optimization,backpressure algorithm is used to conduct route planning and load balancing across edge clouds.In small-scale optimization,game-theoretic multi-agent learning is exploited to perform regional resource allocation.The experimental results show that the proposed algorithm outperforms the baseline algorithms which optimize route planning and resource allocation separately.

关键词： connected vehicles edge computing resource allocation route planning

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Weapon configuration, allocation and route planning with time windows for multiple unmanned combat air vehicles

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Journal of Systems Engineering and Electronics 2018年第5期29卷 953-968页

作者： ZHANG Jiaming LIU Zhong SHI Jianmai CHEN Chao Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology

Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

关键词： unmanned combat air vehicles(UCAVs) mission planning route planning weapon configuration time windows

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Landing route planning method for micro drones based on hybrid optimization algorithm

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Biomimetic Intelligence & Robotics 2021年第1期1卷 26-30页

作者： Liang Shaoran Song Bifeng Xue Dong School of Aeronautics Northwestern Polytechnical UniversityXi’anChina

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone,this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method.The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms.In the process of landing route planning,the planning plane is first obtained by making the gliding plane tangent to the obstacle.In the planning plane,the projection of obstacle is transformed into multiple unreachable line segments.By designing an optimization model,the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem.Taking the shortest route as the optimization objective,the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle.During the optimization process,through the orthogonal learning of the intermediate iterative results,the hybrid algorithm can adaptively select the next iterative algorithm,so it can give full play to the respective advantages of the two algorithms.The optimization results show that,compared with the single optimization algorithm,the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.

关键词： route planning Micro drones Dragonfly optimization Hybrid optimization

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route planning for Surveying Major Roads after Natural Disasters

Route Planning for Surveying Major Roads after Natural Disas...

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作者： Fengbin Chen Department of Electronics and Communication Engineering North China Electric Power University

After the natural disaster, in addition to the disaster itself, the affected area also faces huge challenges, that is, the collapse of cellular communications, power and transportation networks has brought a lot of inconvenience to the disaster relief work. The surge in demand for medical supplies and the restoration of roads are two aspects that cannot be ignored. The reasonable solution of the above two problems will not only help improve the efficiency of rescue, but also promote the post-disaster reconstruction process. The survey of the main roads in the affected areas is the first step in solving these two problems. In this article, a series of methods have been developed to help plan the route of the survey task. To find the best reconnaissance lines, we learn from the Traveling Salesman Problem（TSP）, and then roughly get the shortest flight routes of the drones carrying the aerial photography device based on the graph and network model. Due to the inevitable errors, we have improved them through the Simulated Annealing（SA） algorithm to achieve the purpose of optimization. We selected Puerto Rico as the research object. By collecting information about Puerto Rico’s geography, climate, major cities and roads, and using the methods described above, the best route for the survey mission was obtained. Through verification, we conclude that this model can solve relatively optimal route planning.

关键词： route planning TSP SA

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route planning of Intelligent Aircraft under Multiple Constraints

Route Planning of Intelligent Aircraft under Multiple Constr...

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作者： Jinling Liu Yue Wang Lijun Hong Xiaochun Hong School of Statistics and Mathematics Yunnan University of Finance and Economics

In the study of multi-constraint route planning,the commonly used methods include genetic algorithm（GA）,particle swarm optimization（PSO） etc.However,these algorithms are often too complex in practical applications and often get local optimal solutions when the number of calculations is too small.In this paper,we proposed the tangential progressive correction（TPC） model to solve the problem of route planning,it’s a kind of guided model with the characteristics of simple realization,good results and high calculation speed for planning aircraft route problem.At the end of the paper,we provided the simulation for relevant data.

关键词： route planning TPC model multiple constraints intelligent aircraft

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Saturation attack based route planning and threat avoidance algorithm for cruise missiles

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Journal of Systems Engineering and Electronics 2011年第6期22卷 948-953页

作者： Guanghui Wang Xuefeng Sun Liping Zhang Chao Lv Department of Command Naval Aeronautical and Astronautical University Yantai 264001 P. R. China Graduate Student Brigade Naval Aeronautical and Astronautical University Yantai 264001 P. R. China

According to the characteristic of cruise missiles,navigation point setting is simplified,and the principle of route planning for saturation attack and a concept of reference route are put forward.With the help of the shortest-tangent idea in route-planning and the algorithm of back reasoning from targets,a reference route algorithm is built on the shortest range and threat avoidance.Then a route-flight-time algorithm is built on navigation points.Based on the conditions of multi-direction saturation attack,a route planning algorithm of multi-direction saturation attack is built on reference route,route-flight-time,and impact azimuth.Simulation results show that the algorithm can realize missiles fired in a salvo launch reaching the target simultaneously from different directions while avoiding threat.

关键词： aerospace system engineering control and navigation technology of aero-craft mission planning route planning cruise missile saturation attack threat avoidance.

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route planning method for UAV in unknown environment based on improved SAS algorithm

Route planning method for UAV in unknown environment based o...

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第32届中国控制与决策会议

作者： Yuanjie Song Xiaojun Xing Qingbiao Xi Bing Yang Ziquan Yu School of Automation Northwest Polytechnical University No.365 Research Institute College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Unmanned aerial vehicle（UAV） real-time target tracking in an unknown environment can be considered as a route planning problem.Aiming at this problem,this paper proposes a three-dimensional（3 D） route planning method based on an improved sparse A* search（SAS） algorithm.This method improves the evaluation function.The actual cost in the evaluation function of the SAS algorithm is removed,and the threat information in the mission environment is constructed as part of the evaluation function to guide the UAV to move away from the threat area.The planned trajectory in this method takes into account the updating of real-time environmental information and can achieve real-time trajectory planning.Furthermore,the filtering effect of singer model,current "statistical" model,and interactive multi-model（IMM） algorithm are compared and the current "statistical" model is finally chosen as the maneuvering target tracking model.

关键词： route planning Target Tracking SAS Algorithm A* Algorithm

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