Mission Planning for 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.

Download Full-text

Multiobjective Mission Planning for UAV under Uncertain Environment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.4435 ◽

2014 ◽

Vol 556-562 ◽

pp. 4435-4438

Author(s):

Jing Yao Zhu ◽

Qi Fang He ◽

Tie Zhu Wang ◽

Zu Tong Wang

Keyword(s):

Unmanned Aerial Vehicles ◽

Uncertainty Theory ◽

Multiobjective Programming ◽

Flight Time ◽

Programming Method ◽

Mission Planning ◽

Planning Problem ◽

Uncertain Environment ◽

Aerial Vehicles ◽

Combat Environment

The combat environment of Unmanned Aerial Vehicles (UAVs) is filled with uncertain factors, which is complex and dynamic. This paper is devoted to the UAV mission planning problem under uncertain environment with three optimization objectives, such as flight time, fuel usage and threat imposed by enemy. Based on the uncertainty theory and multiobjective programming method, the UAV uncertain multiobjective mission plaaning model is built and solved.

Download Full-text

Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

Wireless Communications and Mobile Computing ◽

10.1155/2021/4154787 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Wei Tan ◽

Yong-jiang Hu ◽

Yue-fei Zhao ◽

Wen-guang Li ◽

Xiao-meng Zhang ◽

...

Keyword(s):

Genetic Algorithm ◽

Unmanned Aerial Vehicles ◽

Voronoi Diagram ◽

Pso Algorithm ◽

Mission Planning ◽

Cooperative System ◽

Aerial Vehicles ◽

Rendezvous Problem ◽

Aerial Vehicle ◽

Multi Uav

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

Download Full-text

The Development and Key Technologies of Unmanned Aerial Vehicles Mission Planning System

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.791-793.2104 ◽

2013 ◽

Vol 791-793 ◽

pp. 2104-2107

Author(s):

Hui Feng Zhang ◽

Yan Song Zhang ◽

Wei Ye ◽

Liguo Lan Zhao ◽

Huan Zhi

Keyword(s):

Unmanned Aerial Vehicles ◽

Mission Planning ◽

Planning System ◽

Aerial Vehicles ◽

Technical Content ◽

Key Technologies ◽

Foreign Mission

Mission planning system is one of the essential equipment for unmanned aerial vehicles task implementation. The development of mission planning system is necessary for the improvement of the efficiency and safety of the unmanned aerial vehicles. Comparing with foreign advanced mission planning system, this area in China is relatively undeveloped. This article focuses on the analysis of some of the key technical content of the overview of the development of the foreign mission planning, trying to provide reference to China's development of mission planning system.

Download Full-text

Preflight and Flight Instructions on the Use of Unmanned Aerial Vehicles (UAVs) for Agricultural Applications

EDIS ◽

10.32473/edis-ae535-2019 ◽

2019 ◽

Vol 2019 (6) ◽

pp. 5

Author(s):

Sri Charan Kakarla ◽

Leon De Morais Nunes ◽

Yiannis Ampatzidis

Keyword(s):

Unmanned Aerial Vehicles ◽

Mission Planning ◽

Biological Engineering ◽

Appropriate Use ◽

Aerial Vehicles ◽

Agricultural Applications

This 5-page document provides guidance on the appropriate use of unmanned aerial vehicles (UAVs) for agricultural applications in Florida. It contains step-by-step instructions for preparing a UAV for flight, creating a mission path (using flight mission planning apps), and collecting UAV-based data. Written by Sri Charan Kakarla, Leon De Morais Nunes, and Yiannis Ampatzidis, and published by the UF/IFAS Department of Agricultural and Biological Engineering, November 2019.

Download Full-text

The Office of the Secretary of Defense (OSD) Unmanned Aerial Vehicles (UAV) Common Mission Planning Architecture (CMPA)- An Overview

2nd AIAA "Unmanned Unlimited" Conf. and Workshop & Exhibit ◽

10.2514/6.2003-6522 ◽

2003 ◽

Cited By ~ 1

Author(s):

Dyke Weatherington ◽

Allen Wilson

Keyword(s):

Unmanned Aerial Vehicles ◽

Mission Planning ◽

Aerial Vehicles

Download Full-text

UAV Mission Planning Resistant to Weather Uncertainty

Sensors ◽

10.3390/s20020515 ◽

2020 ◽

Vol 20 (2) ◽

pp. 515 ◽

Cited By ~ 3

Author(s):

Amila Thibbotuwawa ◽

Grzegorz Bocewicz ◽

Grzegorz Radzki ◽

Peter Nielsen ◽

Zbigniew Banaszak

Keyword(s):

Wind Speed ◽

Unmanned Aerial Vehicles ◽

Collision Avoidance ◽

Weather Conditions ◽

Mission Planning ◽

Alternative Strategies ◽

Fleet Size ◽

Aerial Vehicles ◽

Time Period ◽

Weather Uncertainty

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.

Download Full-text

Processing Updated Imagery for Use in Dismounted Mission Planning on NETT Warrior

Industrial and Systems Engineering Review ◽

10.37266/iser.2015v3i2.pp91-97 ◽

2015 ◽

Vol 3 (2) ◽

pp. 91-97

Author(s):

Gene Fogh ◽

Chris Gramling ◽

Mitch Hansen ◽

Ella Mason ◽

Davie Chennault ◽

...

Keyword(s):

Unmanned Aerial Vehicles ◽

Real Time ◽

Satellite Imagery ◽

Short Range ◽

Mission Planning ◽

Large Area ◽

Small Unit ◽

Aerial Vehicles ◽

Effective Manner ◽

Mobile Maps

Small unit leaders need real time images to aid in planning missions and making decisions. Currently, this ability partially exists through the use of short range Unmanned Aerial Vehicles (UAV) like Raven and Shadow, which provide unprocessed imagery. There is a limited capability to gather imagery for a large area, process it into a useable format, and deliver the product to the small unit leader. Android-based platforms like Nett Warrior currently use older, primarily satellite, imagery to depict the battle space. We are creating a process that takes up to date satellite and UAV imagery in a format that is available as an offline map and use the Nett Warrior platform to deliver this imagery to small unit leaders. This enhanced imagery will allow leaders to plan missions in a more effective manner due to the updated imagery and the benefits mobile maps provide.

Download Full-text

UAV MISSION PLANNING FOR AUTOMATIC EXPLORATION AND SEMANTIC MAPPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b1-2020-521-2020 ◽

2020 ◽

Vol XLIII-B1-2020 ◽

pp. 521-526

Author(s):

Y. Dehbi ◽

L. Klingbeil ◽

L. Plümer

Keyword(s):

Unmanned Aerial Vehicles ◽

Prior Knowledge ◽

Collision Detection ◽

A Priori ◽

Mission Planning ◽

Semantic Mapping ◽

Reasoning Process ◽

Terrain Models ◽

Aerial Vehicles ◽

Building Models

Abstract. Unmanned Aerial Vehicles (UAVs) are used for the inspection of areas which are otherwise difficult to access. Autonomous monitoring and navigation requires a background knowledge on the surroundings of the vehicle. Most mission planing systems assume collision-free pre-defined paths and do not tolerate a GPS signal outage. Our approach makes weaker assumptions. This paper introduces a mission planing platform allowing for the integration of environmental prior knowledge such as 3D building and terrain models. This prior knowledge is integrated to pre-compute an octomap for collision detection. The semantically rich building models are used to specify semantic user queries such as roof or facade inspection. A reasoning process paves the way for semantic mission planing of hidden and a-priori unknown objects. Subsequent scene interpretation is performed by an incremental parsing process.

Download Full-text