scholarly journals Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution

2020 ◽  
Vol 24 (7) ◽  
pp. 820-828
Author(s):  
Hao Zhang ◽  
Lihua Dou ◽  
Chunxiao Cai ◽  
Bin Xin ◽  
◽  
...  
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Dynamic Properties ◽  
Three Dimensional ◽  
Route Planning ◽  
Performance Comparison ◽  
Solution Quality ◽  
De Algorithm ◽  
Planning Algorithm ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.

MISSION-ADAPTABLE ROUTE PLANNING IN UNCERTAIN AND ADVERSARIAL ENVIRONMENT

2006 ◽  
Vol 15 (05) ◽  
pp. 803-821 ◽  
Author(s):  
PING YAN ◽  
MINGYUE DING ◽  
CHANGWEN ZHENG
Keyword(s):  
Real Time ◽  
A Priori ◽  
Route Planning ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Solution Quality ◽  
Model Complex ◽  
Local Methods ◽  
Domain Specific Knowledge ◽  
Planning Algorithm

In this paper, the route-planning problems of Unmanned Aerial Vehicle (UAV) in uncertain and adversarial environment are addressed, including not only single-mission route planning in known a priori environment, but also the route replanning in partially known and mission-changeable environments. A mission-adaptable hybrid route-planning algorithm based on flight roadmap is proposed, which combines existing global and local methods (Dijkstra algorithm, SAS and D*) into a two-level framework. The environment information and constraints for UAV are integrated into the procedure of building flight roadmap and searching for routes. The route-planning algorithm utilizes domain-specific knowledge and operates in real time with near-optimal solution quality, which is important to uncertain and adversarial environment. Other planners do not provide all of the functionality, namely real-time planning and replanning, near-optimal solution quality, and the ability to model complex 3D constraints.

scholarly journals Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5712
Author(s):  
Wojciech Stecz ◽  
Krzysztof Gromada
Keyword(s):  
Planning Process ◽  
Route Planning ◽  
Mixed Integer ◽  
Military Operations ◽  
Flight Trajectory ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Milp Model ◽  
Flight Trajectories ◽  
Synthetic Aperture Radars

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.

scholarly journals Optimization Shape of Variable Capacitance Micromotor Using Differential Evolution Algorithm

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
A. Ketabi ◽  
M. J. Navardi
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Fem Analysis ◽  
Torque Ripple ◽  
Optimum Shape ◽  
De Algorithm ◽  
Variable Capacitance ◽  
Fitness Value ◽  
Lower Torque

A new method for optimum shape design of variable capacitance micromotor (VCM) using Differential Evolution (DE), a stochastic search algorithm, is presented. In this optimization exercise, the objective function aims to maximize torque value and minimize the torque ripple, where the geometric parameters are considered to be the variables. The optimization process is carried out using a combination of DE algorithm and FEM analysis. Fitness value is calculated by FEM analysis using COMSOL3.4, and the DE algorithm is realized by MATLAB7.4. The proposed method is applied to a VCM with 8 poles at the stator and 6 poles at the rotor. The results show that the optimized micromotor using DE algorithm had higher torque value and lower torque ripple, indicating the validity of this methodology for VCM design.

Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

Robotica ◽  
2020 ◽  
pp. 1-16
Author(s):  
Xiaofeng Liu ◽  
Jian Ma ◽  
Dashan Chen ◽  
Li-Ye Zhang
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Route Optimization ◽  
Road Segment ◽  
Solution Quality ◽  
Planning Approach ◽  
Aerial Vehicle ◽  
Road Segments

SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

scholarly journals A Novel Multi-Objective and Multi-Constraint Route Recommendation Method Based on Crowd Sensing

2021 ◽  
Vol 11 (21) ◽  
pp. 10497
Author(s):  
Xiaoyao Zheng ◽  
Yonglong Luo ◽  
Liping Sun ◽  
Qingying Yu ◽  
Ji Zhang ◽  
...  
Keyword(s):  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Route Planning ◽  
Neighborhood Search ◽  
Scoring Method ◽  
Multi Objective ◽  
Crowd Sensing ◽  
Planning Algorithm ◽  
Route Recommendation ◽  
Public Datasets

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people’s personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time.

Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm

2017 ◽  
Vol 266 ◽  
pp. 445-457 ◽  
Author(s):  
Chen YongBo ◽  
Mei YueSong ◽  
Yu JianQiao ◽  
Su XiaoLong ◽  
Xu Nuo
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Search Algorithm ◽  
Three Dimensional ◽  
Aerial Vehicle ◽  
Vehicle Path

A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms

2016 ◽  
Vol 231 (14) ◽  
pp. 2690-2705 ◽  
Author(s):  
WY Lin ◽  
KM Hsiao
Keyword(s):  
Differential Evolution ◽  
Synthesis Method ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Dimensional Synthesis ◽  
Solution Quality ◽  
De Algorithm ◽  
Mutation Operators ◽  
Mutation Strategy ◽  
Solution Accuracy

A one-phase synthesis method using heuristic optimization algorithms can solve the dimensional synthesis problems of path-generating four-bar mechanisms. However, due to the difficulty of the problem itself, there is still room for improvement in solution accuracy and reliability. Therefore, in this study, a new differential evolution (DE) algorithm with a combined mutation strategy, termed the combined-mutation differential evolution (CMDE) algorithm, is proposed to improve the solution quality. In the combined mutation strategy, the DE/best/1 operator and the DE/current-to-best/1 operator are respectively executed on some superior parents and some mediocre parents, and the DE/rand/1 operator is executed on the other inferior parents. Furthermore, the individuals participating in the three mutation operators are randomly selected from the entire set of parents. The proposed CMDE algorithm with the three different search modes possesses better population diversity as well as search ability than the DE algorithm. The effectiveness of the proposed CMDE algorithm is demonstrated using five representative problems. Findings show a marked improvement in solution accuracy and reliability. The most accurate results are obtained with an approximate combination ratio for the three mutation operators.

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