A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

2015 ◽  
Vol 03 (03) ◽  
pp. 205-219 ◽  
Author(s):  
Jingjing Wang ◽  
Y. F. Zhang ◽  
L. Geng ◽  
J. Y. H. Fuh ◽  
S. H. Teo
Keyword(s):  
Task Scheduling ◽  
Search Algorithm ◽  
Time Windows ◽  
Task Assignment ◽  
Flight Path ◽  
Mission Planning ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Good Plan

This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.

scholarly journals A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 443 ◽  
Author(s):  
Zhe Zhao ◽  
Jian Yang ◽  
Yifeng Niu ◽  
Yu Zhang ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search Algorithm ◽  
Pseudospectral Method ◽  
Task Assignment ◽  
Mission Planning ◽  
Planning Problem ◽  
B Spline ◽  
Aerial Vehicles ◽  
Third Stage ◽  
Multiple Unmanned Aerial Vehicles

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

Emerging approaches to support dynamic mission planning: survey and recommendations for future research

2020 ◽  
pp. 154851291989875
Author(s):  
Matthew Henchey ◽  
Scott Rosen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Department Of Defense ◽  
Flight Time ◽  
Flight Path ◽  
Mission Planning ◽  
Future Research ◽  
Problem Space ◽  
New Information ◽  
Algorithmic Support ◽  
Aerial Vehicle

In the Department of Defense, unmanned aerial vehicle (UAV) mission planning is typically in the form of a set of pre-defined waypoints and tasks, and results in optimized plans being implemented prior to the beginning of the mission. These include the order of waypoints, assignment of tasks, and assignment of trajectories. One emerging area that has been recently identified in the literature involves frameworks, simulations, and supporting algorithms for dynamic mission planning, which entail re-planning mid-mission based on new information. These frameworks require algorithmic support for flight path and flight time approximations, which can be computationally complex in nature. This article seeks to identify the leading academic algorithms that could support dynamic mission planning and recommendations for future research for how they could be adopted and used in current applications. A survey of emerging UAV mission planning algorithms and academic UAV flight path algorithms is presented, beginning with a taxonomy of the problem space. Next, areas of future research related to current applications are presented.

scholarly journals A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

2019 ◽  
Vol 9 (7) ◽  
pp. 1470 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Flight Path ◽  
Computational Time ◽  
Minimum Length ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Path Planning Algorithm

This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

scholarly journals An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1814
Author(s):  
An-Di Tang ◽  
Tong Han ◽  
Huan Zhou ◽  
Lei Xie
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Optimization Problem ◽  
Fitness Function ◽  
Population Diversity ◽  
Planning Problem ◽  
Turn Angle ◽  
Set Constraints ◽  
Planning Algorithm ◽  
Aerial Vehicle

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.

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.

scholarly journals A minimum-time obstacle-avoidance path planning algorithm for unmanned aerial vehicles

2021 ◽  
Author(s):  
Arturo De Marinis ◽  
Felice Iavernaro ◽  
Francesca Mazzia
Keyword(s):  
Path Planning ◽  
Minimum Principle ◽  
Three Dimensional ◽  
Planning Problem ◽  
Pontryagin Minimum Principle ◽  
New Strategy ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Vehicle Trajectory ◽  
Path Planning Algorithm

AbstractIn this article, we present a new strategy to determine an unmanned aerial vehicle trajectory that minimizes its flight time in presence of avoidance areas and obstacles. The method combines classical results from optimal control theory, i.e. the Euler-Lagrange Theorem and the Pontryagin Minimum Principle, with a continuation technique that dynamically adapts the solution curve to the presence of obstacles. We initially consider the two-dimensional path planning problem and then move to the three-dimensional one, and include numerical illustrations for both cases to show the efficiency of our approach.

scholarly journals An Improved Cooperative Control Method for Hybrid Unmanned Aerial-Ground System in Multitasks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Ni ◽  
Min Tang ◽  
Yinan Chen ◽  
Weidong Cao
Keyword(s):  
Cooperative Control ◽  
Control Method ◽  
Search Algorithm ◽  
Dynamic Programming Algorithm ◽  
Task Assignment ◽  
Programming Algorithm ◽  
Planning Problem ◽  
Three Stages ◽  
Time And Energy ◽  
Relay Stations

The cooperative control in complex multitasks using unmanned aerial vehicle and unmanned ground vehicle (UAV/UGV) is an important and challenging issue in the multirobot cooperative field. The main goal of the task studied in this paper is to minimize the time and energy consumed by the system to complete the assigned tasks. In this paper, a complex multitask problem using the hybrid UAV/UGV system is studied, which is divided into three stages, namely, the stage of finding the optimal locations of the relay stations for the UGV; the stage of solving the path planning problem for the UGV; and the stage of the task assignment for multi-UAVs. Furthermore, an improved integrated method is proposed to deal with the cooperative control problems in these three stages. Firstly, an adaptive clustering method is proposed to determine the locations of the UGV relay stations, and then an improved cuckoo search algorithm is used to find the shortest path for the UGV. Finally, a grouping method is presented to solve the multitask allocation problem of UAVs, based on an improved dynamic programming algorithm. In addition, some simulations are carried out and the results show that the proposed method has better performance when it comes to the time and energy consumption and can effectively guide the hybrid UAV/UGV system to carry out the complex multitasks.

scholarly journals Efficient Energy Flight Path Planning Algorithm Using 3-D Visibility Roadmap for Small Unmanned Aerial Vehicle

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Zahoor Ahmad ◽  
Farman Ullah ◽  
Cong Tran ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Energy Efficient ◽  
Flight Path ◽  
Considerable Time ◽  
3 Dimensional ◽  
Efficient Energy ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Path Planning Algorithm

This paper presents the flight path planning algorithm in a 3-dimensional environment with fixed obstacles for small unmanned aerial vehicles (SUAVs). The emergence of SUAVs for commercial uses with low-altitude flight necessitates efficient flight path planning concerning economical energy consumption. We propose the visibility roadmap based on the visibility graph approach to deal with this uprising problem. The objective is to approximate the collision-free and energy-efficient flight path of SUAVs for flight missions in a considerable time complexity. Stepwise, we describe the construction of the proposed pathfinding algorithm in a convex static obstacle environment. The theoretical analysis and simulation results prove the effectiveness of our method.

A constraint-based approach for planning unmanned aerial vehicle activities

2016 ◽  
Vol 31 (5) ◽  
pp. 486-497
Author(s):  
Christophe Guettier ◽  
François Lucas
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicles ◽  
User Acceptance ◽  
Mission Planning ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Mission Duration ◽  
Aerial Vehicle ◽  
Global Planning ◽  
And Control

AbstractUnmanned Aerial Vehicles (UAV) represent a major advantage in defense, disaster relief and first responder applications. UAV may provide valuable information on the environment if their Command and Control (C2) is shared by different operators. In a C2 networking system, any operator may request and use the UAV to perform a remote sensing operation. These requests have to be scheduled in time and a consistent navigation plan must be defined for the UAV. Moreover, maximizing UAV utilization is a key challenge for user acceptance and operational efficiency. The global planning problem is constrained by the environment, targets to observe, user availability, mission duration and on-board resources. This problem follows previous research works on automatic mission Planning & Scheduling for defense applications. The paper presents a full constraint-based approach to simultaneously satisfy observation requests, and resolve navigation plans.

Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

2019 ◽  
Vol 233 (12) ◽  
pp. 4571-4582 ◽  
Author(s):  
Hanjie Hu ◽  
Yu Wu ◽  
Jinfa Xu ◽  
Qingyun Sun
Keyword(s):  
Urban Environment ◽  
Trajectory Planning ◽  
Cuckoo Search ◽  
General Idea ◽  
Planning Problem ◽  
Safety Level ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Express Industry ◽  
Easy Operation

Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.

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