Route Planning Method Research Articles

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.

A New Transportation Route Planning Method for Wind-Based Hydrogen Supply Chains

A New Transportation Route Planning Method for Wind-Based Hydrogen Supply Chains

A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.

A Route Planning Method using Neural Network and HIL Technology Applied for Cargo Ships

This paper presents the development of a method to find optimal routes for cargo ships with three criteria: fuel consumption, safety, and required time. Unlike most previous works, operational data are used for the studies. In this study, we use data collected from a hardware-in-loop (HIL) simulator, with the plant model being a 3D dynamic model of a bulk carrier designed and programmed from 6 degrees of freedom (6-DOF) equations that can interact with forces and moments from the environmental disturbances. The dataset generated from the HIL simulator with various operating scenarios is used to train an artificial neural network (ANN) model. This predictive model then combines the A* algorithm, weather forecast data, ship parameters, and waypoint coordinates to find the optimal routes for ships before each voyage. The test results show that the proposed method works reliably, helping to improve fuel efficiency and enhance the safety of the ships.

Intelligent Route Planning Method for UAV Based on Swarm Intelligence and Deep Learning Technology

Intelligent Route Planning Method for UAV Based on Swarm Intelligence and Deep Learning Technology

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

Tour Route Recommendation Model by the Improved Symmetry-Based Naive Bayes Mining and Spatial Decision Forest Search

In machine learning, classifiers have the feature of constant symmetry when performing the attribute transformation. In the research field of tourism recommendation, tourists’ interests should be mined and extracted by the symmetrical transformation in founding the training dataset and creating the classifier, so as to ensure that the recommendation results meet the individualized interests and needs. In this paper, by applying the feature of constant symmetry in the classifier and analyzing the research background and existing problems of POI tour routes, we propose and construct a tour route recommendation model using improved symmetry-based Naive Bayes mining and spatial decision forest search. First, the POI natural attribute classification model is constructed based on text mining to classify the natural attributes of the destination POIs. Second, the destination POI recommendation model based on the improved symmetry-based Naive Bayes mining and decision forest algorithm is constructed, outputting POIs that match tourists’ interests. On this basis, the POI tour route recommendation model based on a spatial decision tree algorithm is established, which outputs the optimal tour route with the lowest sub-interval cost and route interval cost. Finally, the validation and comparative experiments are designed to output the optimal POIs and tour routes by using the proposed algorithms, and then the proposed algorithm is compared with the commonly used route planning methods, GDM and 360M. Experimental results show that the proposed algorithm can reduce travel costs by 4.56% and 10.36%, respectively, on the optimal tour route compared to the GDM and 360M and by 2.94% and 8.01%, respectively, on the suboptimal tour route compared to the GDM and 360M, which verifies the advantages of the proposed algorithm over the traditional route planning methods.

A Route Planning Method for UAV Swarm Inspection of Roads Fusing Distributed Droneport Site Selection.

Current methods that use Unmanned Aerial Vehicle (UAV) swarms to inspect roads still have many limitations in practical applications, such as the lack of or difficulty in the route planning, the unbalanced utilization rate of the UAV swarm and the difficulty of the site selection for the distributed droneports. To solve the limitations, firstly, we construct the inspection map and remove the redundant information irrelevant to the road inspection. Secondly, we formulate both the route planning problem and the droneport site selection problem in a unified multi-objective optimization model. Thirdly, we redesign the encoding strategy, the updating rules and the decoding strategy of the particle swarm optimization method to effectively solve both the route planning problem and the droneport site selection problem. Finally, we introduce the comprehensive evaluation indicators to verify the effectiveness of the route planning and the droneport site selection. The experimental results show that (1) with the proposed method, the overlapped part of the optimized inspection routes is less than 7% of the total mileage, and the balanced utilization rate of the UAVs is above 75%; (2) the reuse rate of the distributed droneports is significantly improved after optimization; and (3) the proposed method outperforms the ant colony optimization (ACO) method in all evaluation indicators. To this end, the proposed method can effectively plan the inspection routes, balance the utilization of the UAVs and select the sites for the distributed droneports, which has great significance for a fully autonomous UAV swarm inspection system for road inspection.

An Efficient Maritime Route Planning Method Based on an Improved A* with an Adaptive Heuristic Function and Parallel Computing Structure

Maritime route planning under minimal-risk conditions plays an important part in the development and utilization of marine resources. High-resolution weather forecasting data places higher demands on the algorithms’ ability to optimize and compute, and existing algorithms are significantly deficient in these aspects. Therefore, we propose a parallel computing-based planning method, segment parallel A* (SPA*), which splits the path into small segments and runs A* separately on CPU cores through a control algorithm. In segment planning, we propose an adaptive heuristic function on A*. It automatically balances the order of magnitude difference between the risk assessment value and the estimated distance, thus significantly reducing the A* expansion useless grid to improve the performance and running speed of the algorithm. Finally, the complete route is obtained by splicing the above segments. In the static planning experiments, the time of SPA* is reduced by about 5~12,425 times compared with 6 traditional and swarm intelligence-based algorithms, i.e., Dijkstra, A*, bidirectional A* (BA*), ant colony optimization (ACO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA). And the abilities to control the risk caused by wind and waves and the comprehensive risk are improved by 7.68%~25.14% and 8.44%~14.38%, respectively; in the dynamic planning experiments, the above results are 4.8~1262.9 times, 3.87%~9.47% and 7.21%~10.36%, respectively. By setting the recommended range of the number of segments for each case, SPA* shows stable performance in terms of the calculation and risk control. SPA* demonstrates a unique structure for using parallel computing in route planning, which is representative and general in both reducing time and improving efficiency.

Automatic Collision Avoidance Route Planning Method in Complex Navigation Environments

Efficient collision avoidance (CA) route planning is one of the important technologies for ensuring the safety of autonomous ships. Many CA route planning studies have been developed for the open sea, but few studies have been conducted for ships in complex navigation environments. A complex navigation environment refers to water encompassing various factors, such as multiple obstacles, varying water depths, ships collision risk, and other maritime challenges. These factors collectively contribute to the complexity of the environment. CA planning in this type of environment is a special type of route planning. To address CA in complex navigation environments, firstly, the route binary tree algorithm is utilized for conducting global route planning in order to determine the optimal path. Secondly, this paper proposes a dynamic generation of local collision avoidance trajectories along the optimal path, seamlessly integrating global route optimization and local collision avoidance. Finally, a comparative analysis is conducted using classical techniques to verify the effectiveness of the proposed method. Numerical simulation results reveal that when a ship is taken as the object of CA in the open sea, the route generated using the proposed method exhibits a remarkable level of unity with the one generated by the benchmark method of improved Artificial Potential Field (APF), particularly in terms of collision avoidance strategy selection, collision avoidance trends, and the actual trajectory. The former’s computational efficiency is improved by at least 30%. When dealing with CA at sea, with complex navigation obstacles, the proposed method considers static complex navigation obstacles, dynamic ships, and their unpredictable strategies to timeously generate a safe path which considers some key rules from the COLREGS (International Regulations for Preventing Collisions at Sea) (IMO, 1972).

A Ship Route Planning Method under the Sailing Time Constraint

This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.

Collaborative route optimization and resource management strategy for multi-target tracking in airborne radar system

In this paper, a collaborative route optimization and resource management (CRO-RM) strategy is developed for multi-target tracking (MTT) in airborne radar system. The mechanism of the proposed CRO-RM strategy is to collaboratively coordinate the kinematic velocity, heading angle, transmit power, waveform pulselength and bandwidth of the airborne radar, in order to achieve accurate target state estimation while adhering to platform limitations and system resources budget constraints. The analytical expression for the Bayesian Cramér-Rao lower bound (BCRLB) on resources optimization and route planning parameters is derived and utilized as the optimization criterion for the CRO-RM strategy. It is shown that the resulting optimization problem is nonlinear and nonconvex since the above adaptable parameters are coupled in the optimization function and constraints. By exploiting the partition technique and cyclic minimization method, embed with the barrier function method for route planning, the semi-definite programming (SDP) for transmit power allocation and the finite grid search for waveform parameters selection, a partition-based four-step solution method is proposed to tackle the optimization problem effectively. Simulation results demonstrate that the proposed CRO-RM strategy achieves the best MTT performance compared with other benchmarks.

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines

The purpose of the work is to improve the accuracy and efficiency of a power line section inspection for a fault detection using unmanned aerial vehicles. The goal was achieved by using a unified computing and measurement platform on multicopter and aircraft drones and by simplifying the interaction between them and by using the inter-object navigation sensors. The most significant results were the development of a method of route planning by drones over different parts of the power grid and a method of inter-object navigation. The drone route planning problem was represented by a multiagent variation of the classical traveling salesman problem and was solved by the ant colony method. The method of inter-object navigation was distinguished by the representation of the power grid topology by high and low intensity graphs, involving a different number and types of drones in the inspection process. The application of the developed methods made it possible to increase the accuracy of power line inspections by 27-73%, and the efficiency by 2-8 times. Solving the problem of multicriteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. Thus, the conducted research has shown the effectiveness of the proposed approach for the monitoring of power facilities, route selection, number and composition of search teams. The direction for further research is to improve the ant algorithm.

Optimal Transshipment Route Planning Method Based on Deep Learning for Multimodal Transport Scenarios

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same.

Optimization of Quadrotor Route Planning with Time and Energy Priority in Windy Environments

Route planning studies are of great importance for quadrotors, which are widely used in military and civil fields, to perform their duties autonomously and efficiently. In the study, a method is proposed that enables Quadrotor Route Planning (QRP) to be optimized with time priority and energy priority by using Genetic Algorithm (GA). For the proposed method, the Wind Effected QRP-App (WEQRP-App) desktop software was developed with the Visual Studio C# programming language. In the developed WEQRP-App, real location information from GMAP.Net map plugin and real wind data from the website of the General Directorate of Meteorology were used. Using Wind Effected Quadrotor Route Planning (WEQRP) method, more realistic planning was made before the flight and the flight efficiency was increased in terms of time and energy priority. Thus, it is foreseen that safer and less costly autonomous flights will take place when compared to the Standard QRP (SQRP) created without taking into account the wind effect, by avoiding the problems that arise due to unexpected energy consumption during the flight mission. When the results obtained from the proposed method were examined, it has been observed that WEQRP provides the improvements up to 13,5% in flight times and up to 27,4% in energy consumption according to SQRP

Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization

Route planning for autonomous driving is a global road planning method based on a given starting point and target point combined with current traffic flow information. The optimal global route can reduce traffic jams and improve the safety and economy of autonomous vehicles. The current optimization method of route planning for autonomous driving only considers a single objective or a chain of single objectives, which cannot meet the requirements of drivers. In this paper, we devise a general framework for the route planning method based on multi-objective optimization. Different from planning optimization based on not only traffic information, the framework considers travel time, distance, cost and personal preference, but focuses more on vehicle status and driver requirements. We use an improved depth-first search algorithm to find the optimal route. The evaluations of our method on real-world traffic data indicate the feasibility and applicability of the framework. Our study contributes to a better understanding of route planning and reveals that exploitation of personal preference can more flexibly configure the corresponding route according to the driver’s requirements.

Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning

In order to improve the motion route planning effect and obstacle avoidance effect of mobile robots, this paper combines the deep learning theory to analyze the motion route planning and obstacle avoidance process of mobile robots. According to the obstacle avoidance trajectory and constraints, this paper establishes a safe distance model for obstacle avoidance, then analyzes the braking process of the robot, and designs an improved safety model for obstacle avoidance. This model integrates two relatively mature safety models, complements their advantages and disadvantages, and comprehensively considers robot safety and the utilization of the motion path. According to the simulation test research, the robot based on deep learning proposed in this paper has a good motion route planning effect and obstacle avoidance effect and can effectively improve the autonomous motion effect of the robot.

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning

Abstract Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).