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.

The Frontier of Route Planning Algorithm for UAV

2014 ◽  
Vol 926-930 ◽  
pp. 3577-3580
Author(s):  
Na Lin ◽  
Ya Lun Zhang
Keyword(s):  
Planning Process ◽  
Route Planning ◽  
Research Status ◽  
The Future ◽  
Planning Algorithm ◽  
At Home

In recent years, as the UAVs route planning in the war becomes more and more important, many scholars pay much attention to its research both at home and abroad. Route planning of UAV is the key of successfully complete task and achieve the target of protection. In the route planning process, in order to obtain an optimal or suboptimal route, we needs to consider the terrain, threat, body performance and many other factors. In this paper, we talk the research background of route planning and research status at home and abroad. At last, we give a brief summary for the trend of route planning in the future.

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 Environment mapping, map constructing, and path planning for underwater navigation of a low-cost µAUV in a cluttered nuclear storage pond

2019 ◽  
Vol 8 (4) ◽  
pp. 277
Author(s):  
Yibin Peng ◽  
Peter N. Green
Keyword(s):  
Path Planning ◽  
Planning Process ◽  
Autonomous Underwater Vehicle ◽  
Low Cost ◽  
Route Planning ◽  
Sonar Sensors ◽  
Underwater Environment ◽  
Novel Approach ◽  
Integrate Environment ◽  
Planning Algorithm

This paper presents a novel approach that enables a low-cost µAUV (micro autonomous underwater vehicle) navigate and work safely in an enclosed cluttered underwater environment. In order to achieve a autonomy collision-free navigation in underwater, it requires a reliable approach that would allow a µAUV which equipped with sparse and inaccurate sonar sensors to map the underwater environment, a map constructing algorithm that converts the obtained data to useful information to establish a map, and a path planning algorithm that plans a collision-free path from the start to the desired goal. The proposed approach will integrate environment survey, environment reconstruction, and path planning as a completed task, which involves map acquisition and path planning. A complete simulation of the environment topology acquisition and route planning process is documented in this paper.

scholarly journals A Framework for Planning and Execution of Drone Swarm Missions in a Hostile Environment

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4150
Author(s):  
Barbara Siemiatkowska ◽  
Wojciech Stecz
Keyword(s):  
Real Time ◽  
Planning Process ◽  
Route Planning ◽  
Mixed Integer ◽  
Hostile Environment ◽  
Ir Camera ◽  
Rule Based System ◽  
Planning Framework ◽  
Mobile Computers

This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.

An Evolutionary Trajectory Planning Algorithm for Multi-UAV-Assisted MEC System

2021 ◽  
Author(s):  
Muhammad Asim ◽  
Wali Khan ◽  
Samir Brahim Belhaouari
Keyword(s):  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Mixed Integer ◽  
Phase Variable ◽  
Mobile Edge Computing ◽  
Evolutionary Trajectory ◽  
Multiple Uavs ◽  
Planning Algorithm ◽  
Aerial Vehicle

Abstract This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.

scholarly journals UAV Mission Planning with SAR Application

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1080 ◽  
Author(s):  
Wojciech Stecz ◽  
Krzysztof Gromada
Keyword(s):  
Time Window ◽  
Scheduling Algorithm ◽  
Time Windows ◽  
Route Planning ◽  
Mission Planning ◽  
Mixed Integer ◽  
Technical Parameters ◽  
Aerial Vehicle ◽  
Flight Route ◽  
Electronic Intelligence

The paper presents the concept of mission planning for a short-range tactical class Unmanned Aerial Vehicle (UAV) that recognizes targets using the sensors it has been equipped with. Tasks carried out by such systems are mainly associated with aerial reconnaissance employing Electro Optical (EO)/Near Infra-Red (NIR) heads, Synthetic Aperture Radar (SAR), and Electronic Intelligence (ELINT) systems. UAVs of this class are most often used in NATO armies to support artillery actions, etc. The key task, carried out during their activities, is to plan a reconnaissance mission in which the flight route will be determined that optimally uses the sensors’ capabilities. The paper describes the scenario of determining the mission plan and, in particular, the UAV flight routes to which the recognition targets are assigned. The problem was decomposed into several subproblems: assigning reconnaissance tasks to UAVs with choosing the reconnaissance sensors and designating an initial UAV flight plan. The last step is planning a detailed flight route taking into account the time constraints imposed on recognition and the characteristics of the reconnaissance sensors. The final step is to generate the real UAV flight trajectory based on its technical parameters. The algorithm for determining exact flight routes for the indicated reconnaissance purposes was also discussed, taking into account the presence of enemy troops and available air corridors. The task scheduling algorithm—Vehicle Route Planning with Time Window (VRPTW)—using time windows is formulated in the form of the Mixed Integer Linear Problem (MILP). The MILP formulation was used to solve the UAV flight route planning task. The algorithm can be used both when planning individual UAV missions and UAV groups cooperating together. The approach presented is a practical way of establishing mission plans implemented in real unmanned systems.

scholarly journals A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199858
Author(s):  
Gianpaolo Gulletta ◽  
Eliana Costa e Silva ◽  
Wolfram Erlhagen ◽  
Ruud Meulenbroek ◽  
Maria Fernanda Pires Costa ◽  
...  
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Planning Process ◽  
Computational Cost ◽  
Humanoid Robots ◽  
Efficient Manner ◽  
Daily Lives ◽  
Human Motor Control ◽  
Planning Algorithm ◽  
Human Motor

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

scholarly journals Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

2014 ◽  
Vol 18 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Johanna C Gerdessen ◽  
Olga W Souverein ◽  
Pieter van ‘t Veer ◽  
Jeanne HM de Vries
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Selection Process ◽  
Saturated Fat ◽  
Mixed Integer ◽  
Monounsaturated Fat ◽  
Food Items ◽  
Milp Model ◽  
Selection Of

AbstractObjectiveTo support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.DesignSelection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.ResultsThe food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.ConclusionsThe results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

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