Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of d...Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.展开更多
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...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.展开更多
With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,l...With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.展开更多
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 UCA...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.展开更多
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 c...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.展开更多
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 ...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.展开更多
A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not ...A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.展开更多
To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planni...To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm (MOGA) for multiple objectives traveling salesman problem (MOTSP). Then, the path between two route nodes is generated based on the heuristic path planning method A *. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.展开更多
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...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).展开更多
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 differe...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.展开更多
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 z...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.展开更多
During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the r...During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.展开更多
In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization ( HPSO ) algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to div...In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization ( HPSO ) algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing (SA) algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.展开更多
Urban cycling environment is an important part of urban slow-moving system,and reasonable route planning is beneficial to the health of residents.In order to improve residents’ cycling quality and enrich travel exper...Urban cycling environment is an important part of urban slow-moving system,and reasonable route planning is beneficial to the health of residents.In order to improve residents’ cycling quality and enrich travel experience,the evaluation system of cycling quality in the main urban area of Handan City was established from the four aspects of convenience,safety,comfort and experience.According to different travel needs,the cycling evaluation index of road sections was calculated and modified to obtain the cycling time of road sections.The shortest route algorithm was used to generate the best riding route for convenience,the best riding route for safety,the best riding route for comfort,and the best riding route for experience.Cycling evaluation indicators were combined with travel route planning to provide residents with a variety of humanized route options,and effectively improve the cycling quality and travel health of residents,which is of great significance to promoting green travel and improving residents’ health.展开更多
With the development of operationally responsive space(ORS) and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the re...With the development of operationally responsive space(ORS) and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the requirements-tasking-effectscycle from one day to hours, the various resources of the wholedata acquisition chain (including satellites, ground stations, dataprocessing centers, users, etc.) should be taken into an overallconsideration, and the traditional batch task planning mode shouldbe transformed into the user-oriented task planning mode. Consideringthere are many approaches for data acquisition due tothe new techniques of ORS satellite, the data acquisition chaintask planning problem for ORS satellite can be seen as the multimodalroute planning problem. Thereby, a framework is presentedusing label-constrained shortest path technique with the conflictresolution. To apply this framework to solve the ORS satellite taskplanning problem, the preprocessing and the conflict resolutionstrategies are discussed in detail. Based on the above work, theuser-oriented data acquisition chain task planning algorithm forORS satellite is proposed. The exact solution can be obtainedin polynomial time using the proposed algorithm. The simulationexperiments validate the feasibility and the adaptability of the proposedapproach.展开更多
As the proliferation and development of automated container terminal continue,the issues of efficiency and safety become increasingly significant.The container yard is one of the most crucial cargo distribution center...As the proliferation and development of automated container terminal continue,the issues of efficiency and safety become increasingly significant.The container yard is one of the most crucial cargo distribution centers in a terminal.Automated Guided Vehicles(AGVs)that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials,while also maximizing efficiency,is a complex challenge.This research introduces an algorithm that integrates Long Short-Term Memory(LSTM)neural network with reinforcement learning techniques,specifically Deep Q-Network(DQN),for routing an AGV carrying hazardous materials within a container yard.The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials.Utilizing real data from the Meishan Port in Ningbo,Zhejiang,China,the actual yard is first abstracted into an undirected graph.Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored,a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials,which are incorporated into the map as background AGVs.Subsequently,DQN is employed to plan the route for an AGV transporting hazardous materials,aiming to reach its destination swiftly while avoiding encounters with other AGVs.Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs.Compared to the method where hazardous material AGV follow the shortest path to their destination,the avoidance efficiency was enhanced by 3.11%.This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals.Additionally,it provides insights for designing avoidance schemes for autonomous driving AGVs,offering solutions for complex operational environments where safety and efficient navigation are paramount.展开更多
Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are result...Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.展开更多
Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant ...Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital investment.Numerous route planning methods for the mobile robot have been developed and applied.According to our best knowledge,no method offers an optimum solution among the existing methods.Particle Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental circumstances.Among the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots.This paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)algorithm.PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with weights.In order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are applied.The experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.展开更多
Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct...Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.展开更多
文摘Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.
基金supported by the Aeronautical Science Foundation of China (20085584010)
文摘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.
文摘With the rapid development of the global economy, maritime transportation has become much more convenient due to large capacities and low freight. However, this means the sea lanes are becoming more and more crowded,leading to high probabilities of marine accidents in complex maritime environments. According to relevant historical statistics, a large number of accidents have happened in water areas that lack high precision navigation data, which can be utilized to enhance navigation safety. The purpose of this work was to carry out ship route planning automatically, by mining historical big automatic identification system(AIS) data. It is well-known that experiential navigation information hidden in maritime big data could be automatically extracted using advanced data mining techniques;assisting in the generation of safe and reliable ship planning routes for complex maritime environments. In this paper, a novel method is proposed to construct a big data-driven framework for generating ship planning routes automatically, under varying navigation conditions. The method performs density-based spatial clustering of applications with noise first on a large number of ship trajectories to form different trajectory vector clusters. Then, it iteratively calculates its centerline in the trajectory vector cluster, and constructs the waterway network from the node-arc topology relationship among these centerlines. The generation of shipping route could be based on the waterway network and conducted by rasterizing the marine environment risks for the sea area not covered by the waterway network. Numerous experiments have been conducted on different AIS data sets in different water areas, and the experimental results have demonstrated the effectiveness of the framework of the ship route planning proposed in this paper.
基金supported by the National Natural Science Foundation of China(7147117571471174)
文摘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.
基金supported in part by the Natural Science Foundation of China under Grant 61902035 and Grant 61876023in part by the Natural Science Foundation of Shandong Province of China under Grant ZR2020LZH005in part by China Postdoctoral Science Foundation under Grant 2019M660565.
文摘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.
文摘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.
文摘A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.
基金Supported by the National Natural Science Foundation of China(9112001591120010)
文摘To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm (MOGA) for multiple objectives traveling salesman problem (MOTSP). Then, the path between two route nodes is generated based on the heuristic path planning method A *. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration.
基金supported by the National Natural Science Foundation of China and Zhejiang Joint Fund for the Integration of Industrialization and Informatization(Nos.U1809212 and U1909206)the Fundamental Research Funds for the Zhejiang Provincial Universities(No.2021XZZX014)the National Natural Science Foundation of China(No.62088102)。
文摘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).
基金supported by National Natural Science Foundation of China,Grants No.11902103 and 11572255China Postdoctoral Science Foundation Grant 2019M651294。
文摘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.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Science under(Grant no XDA19070101).
文摘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.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011313)in part by Guangdong Innovative and Entrepreneurial Research Team Program(Grant No.2019ZT08Z780)in part by Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents。
文摘During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.
基金Supported by the National Natural Science Foundation of China(91016004)
文摘In order to solve cruise missile route planning problem for low-altitude penetration , a hy- brid particle swarm optimization ( HPSO ) algorithm is proposed. Firstly, K-means clustering algo- rithm is applied to divide the particle swarm into multiple isolated sub-populations, then niche algo- rithm is adopted to make all particles independently search for optimal values in their own sub-popu- lations. Finally simulated annealing (SA) algorithm is introduced to avoid the weakness of PSO algo- rithm, which can easily be trapped into the local optimum in the search process. The optimal value obtained by every sub-population search corresponds to an optimal route, multiple different optimal routes are provided for cruise missile. Simulation results show that the HPSO algorithm has a fast convergence rate, and the planned routes have flat ballisticpaths and short ranges which meet the low-altitude penetration requirements.
基金Sponsored by Social Science Foundation of Hebei Province,China (HB19YS039)。
文摘Urban cycling environment is an important part of urban slow-moving system,and reasonable route planning is beneficial to the health of residents.In order to improve residents’ cycling quality and enrich travel experience,the evaluation system of cycling quality in the main urban area of Handan City was established from the four aspects of convenience,safety,comfort and experience.According to different travel needs,the cycling evaluation index of road sections was calculated and modified to obtain the cycling time of road sections.The shortest route algorithm was used to generate the best riding route for convenience,the best riding route for safety,the best riding route for comfort,and the best riding route for experience.Cycling evaluation indicators were combined with travel route planning to provide residents with a variety of humanized route options,and effectively improve the cycling quality and travel health of residents,which is of great significance to promoting green travel and improving residents’ health.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘With the development of operationally responsive space(ORS) and on-board processing techniques, the end users canreceive the observation data from the ORS satellite directly. Tosatisfy the demand for reducing the requirements-tasking-effectscycle from one day to hours, the various resources of the wholedata acquisition chain (including satellites, ground stations, dataprocessing centers, users, etc.) should be taken into an overallconsideration, and the traditional batch task planning mode shouldbe transformed into the user-oriented task planning mode. Consideringthere are many approaches for data acquisition due tothe new techniques of ORS satellite, the data acquisition chaintask planning problem for ORS satellite can be seen as the multimodalroute planning problem. Thereby, a framework is presentedusing label-constrained shortest path technique with the conflictresolution. To apply this framework to solve the ORS satellite taskplanning problem, the preprocessing and the conflict resolutionstrategies are discussed in detail. Based on the above work, theuser-oriented data acquisition chain task planning algorithm forORS satellite is proposed. The exact solution can be obtainedin polynomial time using the proposed algorithm. The simulationexperiments validate the feasibility and the adaptability of the proposedapproach.
文摘As the proliferation and development of automated container terminal continue,the issues of efficiency and safety become increasingly significant.The container yard is one of the most crucial cargo distribution centers in a terminal.Automated Guided Vehicles(AGVs)that carry materials of varying hazard levels through these yards without compromising on the safe transportation of hazardous materials,while also maximizing efficiency,is a complex challenge.This research introduces an algorithm that integrates Long Short-Term Memory(LSTM)neural network with reinforcement learning techniques,specifically Deep Q-Network(DQN),for routing an AGV carrying hazardous materials within a container yard.The objective is to ensure that the AGV carrying hazardous materials efficiently reaches its destination while effectively avoiding AGVs carrying non-hazardous materials.Utilizing real data from the Meishan Port in Ningbo,Zhejiang,China,the actual yard is first abstracted into an undirected graph.Since LSTM neural network can efficiently conveys and represents information in long time sequences and do not causes useful information before long time to be ignored,a two-layer LSTM neural network with 64 neurons per layer was constructed for predicting the motion trajectory of AGVs carrying non-hazardous materials,which are incorporated into the map as background AGVs.Subsequently,DQN is employed to plan the route for an AGV transporting hazardous materials,aiming to reach its destination swiftly while avoiding encounters with other AGVs.Experimental tests have shown that the route planning algorithm proposed in this study improves the level of avoidance of hazardous material AGV in relation to non-hazardous material AGVs.Compared to the method where hazardous material AGV follow the shortest path to their destination,the avoidance efficiency was enhanced by 3.11%.This improvement demonstrates potential strategies for balancing efficiency and safety in automated terminals.Additionally,it provides insights for designing avoidance schemes for autonomous driving AGVs,offering solutions for complex operational environments where safety and efficient navigation are paramount.
基金This work was supported by the MSIT(Ministry of Science&ICT),Korea,under the ITRC support program(IITP-2021-2017-0-01633).This research work was also supported by the Research Incentive Grant R20129 of Zayed University,UAE。
文摘Airline industry has witnessed a tremendous growth in the recent past.Percentage of people choosing air travel as first choice to commute is continuously increasing.Highly demanding and congested air routes are resulting in inadvertent delays,additional fuel consumption and high emission of greenhouse gases.Trajectory planning involves creation identification of cost-effective flight plans for optimal utilizationof fuel and time.This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required.In this paper,an algorithm for dynamic planning of optimized flight trajectories has been proposed.The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather,aerodynamic drag and air traffic within that virtual cube.There are several constraints like simultaneous flight separation rules,weather conditions like air temperature,pressure,humidity,wind speed and direction that pose a real challenge for calculating optimal flight trajectories.To validate the proposed methodology,a case analysis was undertaken within Indian airspace.The flight routes were simulated for four different air routes within Indian airspace.The experiment results observed a seven percent reduction in drag values on the predicted path,hence indicates reduction in carbon footprint and better fuel economy.
文摘Recently,the path planning problem may be considered one of the most interesting researched topics in autonomous robotics.That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite.A promising route planning for mobile robots on one side saves time and,on the other side,reduces the wear and tear on the robot,saving the capital investment.Numerous route planning methods for the mobile robot have been developed and applied.According to our best knowledge,no method offers an optimum solution among the existing methods.Particle Swarm Optimization(PSO),a numerical optimization method based on the mobility of virtual particles in a multidimensional space,is considered one of the best algorithms for route planning under constantly changing environmental circumstances.Among the researchers,reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots.This paper proposes a PSO Weighted Grey Wolf Optimization(PSOWGWO)algorithm.PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization(GWO)with weights.In order to measure the statistical efficiency of the proposed algorithm,Wilcoxon rank-sum and ANOVA statistical tests are applied.The experimental results demonstrate a 25%to 45%enhancement in terms of Area Under Curve(AUC).Moreover,superior performance in terms of data size,path planning time,and accuracy is demonstrated over other state-of-the-art techniques.
基金supported by the the Youth Science and Technology Innovation Fund (Science)(Nos.NS2014070, NS2014070)
文摘Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.