Efficient Route Planning for Real-Time Demand-Responsive Transit

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.

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

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.

Coordinated Route Planning via Nash Equilibrium and Evolutionary Computation

The coordinated route planning problem for multiple unmanned air vehicles （UAVs）, a cooperative optimization problem, also a non-cooperative game, is addressed in the framework of game theory, A Nash equilibrium based route planner is proposed. The rational is that the structure of UAV subteam usually provides some inherent and implicit preference information, which help to find the optimum coordinated routes and the optimum combination of the various objective functions. The route planner combines the concepts of evolutionary computation with problem-specific chromosome structures and evolutionary operators and handles different kinds of mission constraints in hierarchical style. Cooperation and competition among UAVs are reflected by the definition of fitness function. Simulations validate the feasibility and superiority of the game-theoretic coordinated routes planner.

Multi-objective evolutionary approach for UAV cruise route planning to collect traffic information

Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.

Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.

Big data-driven automatic generation of ship route planning in complex maritime environments

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.

User-oriented data acquisition chain task planningalgorithm for operationally responsive space satellite

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.

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

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.

Integrated Route Planning and Resource Allocation for Connected Vehicles

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.

Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

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.

An Air Route Planning Model of Unmanned Aerial Vehicles Under Constraints of Ground Safety

With the rapid growth of the number and flight time of unmanned aerial vehicles(UAVs),safety accidents caused by UAVs flight risk is increasing gradually.Safe air route planning is an effective means to reduce the operational risk of UAVs at the strategic level.The optimal air route planning model based on ground risk assessment is presented by considering the safety cost of UAV air route.Through the rasterization of the ground surface under the air route,the safety factor of each grid is defined with the probability of fatality on the ground per flight hour as the quantitative index.The air route safety cost function is constructed based on the safety factor of each grid.Then,the total cost function considering both air route safety and flight distance is established.The expected function of the ant colony algorithm is rebuilt and used as the algorithm to plan the air routes.The effectiveness of the new air route planning model is verified through the logistical distribution scenario on urban airspace.The results indicate that the new air route planning model considering safety factor can greatly improve the overall safety of air route under small increase of the total flight time.

Cruise missile multiple routes planning based on hybrid particle swarm optimization

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.

An optimization model of UAV route planning for road segment surveillance

Unmanned aerial vehicle(UAV)was introduced to take road segment traffic surveillance.Considering the limited UAV maximum flight distance,UAV route planning problem was studied.First,a multi-objective optimization model of planning UAV route for road segment surveillance was proposed,which aimed to minimize UAV cruise distance and minimize the number of UAVs used.Then,an evolutionary algorithm based on Pareto optimality technique was proposed to solve multi-objective UAV route planning problem.At last,a UAV flight experiment was conducted to test UAV route planning effect,and a case with three scenarios was studied to analyze the impact of different road segment lengths on UAV route planning.The case results show that the optimized cruise distance and the number of UAVs used decrease by an average of 38.43% and 33.33%,respectively.Additionally,shortening or extending the length of road segments has different impacts on UAV route planning.

Multi-Objective Weather Routing Algorithm for Ships Based on Hybrid Particle Swarm Optimization

Maritime transportation has become an important part of the international trade system.To promote its sustainable de-velopment,it is necessary to reduce the fuel consumption of ships,decrease navigation risks,and shorten the navigation time.Ac-cordingly,planning a multi-objective route for ships is an effective way to achieve these goals.In this paper,we propose a multi-ob-jective optimal ship weather routing system framework.Based on this framework,a ship route model,ship fuel consumption model,and navigation risk model are established,and a non-dominated sorting and multi-objective ship weather routing algorithm based on particle swarm optimization is proposed.To fasten the convergence of the algorithm and improve the diversity of route solutions,a mutation operation and an elite selection operation are introduced in the algorithm.Based on the Pareto optimal front and Pareto optimal solution set obtained by the algorithm,a recommended route selection criterion is designed.Finally,two sets of simulated navigation simulation experiments on a container ship are conducted.The experimental results show that the proposed multi-objective optimal weather routing system can be used to plan a ship route with low navigation risk,short navigation time,and low fuel consumption,fulfilling the safety,efficiency,and economic goals.

An Evolutionary Real-Time 3D Route Planner for Aircraft

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.

Biogeography-Based Combinatorial Strategy for Efficient Autonomous Underwater Vehicle Motion Planning and Task-Time Management

Autonomous Underwater Vehicles （AUVs） are capable of conducting various underwater missions and marine tasks over long periods of time. In this study, a novel conflict-free motion-planning framework is introduced. This framework enhances AUV mission performance by completing the maximum number of highest priority tasks in a limited time through a large-scale waypoint cluttered operating field and ensuring safe deployment during the mission. The proposed combinatorial route-path-planner model takes advantage of the Biogeography- Based Optimization （BBO） algorithm to satisfy the objectives of both higher- and lower-level motion planners and guarantee the maximization of mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios, including cost constraints in time-varying operating fields. To demonstrate the reliability of the proposed model, the performance of each motion planner is separately assessed and statistical analysis is conducted to evaluate the total performance of the entire model. The simulation results indicate the stability of the proposed model and the feasibility of its application to real-time experiments.

Multi-objective route planning approach for timely searching tasks of a supervised robot

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.

Estimating Fuel-Efficient Air Plane Trajectories Using Machine Lear

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.

Research on Route Planning Method Based on Active-Attack Strategy

Aimed at modern high-density,high overlapped and powerful antipersonnel stealthy penetration environment,route planning techniques on active-attack strategy were thoroughly and further studied,in order to get good probability of survival and perfect efficiency of task accomplishment.It provides a new thought for and a new solution to the application of route planning in new era.

Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

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.