This document discusses optimizing flight path planning for unmanned aerial vehicles (UAVs) using grey relational analysis. It establishes an optimal decision-making system and mathematical model for UAV flight paths that considers multiple objectives like minimizing costs and avoiding threats. Grey relational analysis is used to deal with relationships between various cost indicators and constraints to solve the optimization model. The model is applied to a problem involving 17 radar threats, 5 missile threats, 10 artillery threats, and 2 climate threats to obtain an optimal flight path.
Uav route planning for maximum target coveragecseij
Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of
the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the
targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of
UAVs to cover maximum number of targets considering their flight range.
The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant
System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach,
based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success
of the proposed MMAS method by increasing the number of covered targets compared to the solution based
on the NN heuristic.
Vehicle Headway Distribution Models on Two-Lane Two-Way Undivided RoadsAM Publications
The time headway between vehicles is an important flow characteristic that affects the safety, level of service, driver behavior, and capacity of a transportation system. The present study attempted to identify suitable probability distribution models for vehicle headways on 2-lane 2-way undivided (2/2 UD) road sections. Data was collected from three locations in the city of Semarang: Abdulrahman Saleh St. (Loc. 1), Taman Siswa St. (Loc. 2) and Lampersari St. (Loc.3). The vehicle headways were grouped into one-second interval. Three mathematical distributions were proposed: random (negative-exponential), normal, and composite, with vehicle headway as variable. The Kolmogorov-Smirnov test was used for testing the goodness of fit. Traffic flows at the selected locations were considered low, with traffic volume ranged between 400 to 670 vehicles per hour per lane. The traffic volume on Loc.1 was 484 vehicles per hour, that on Loc. 2 was 405 vehicles per hour, and that on Loc. 3 was 666 vehicles per hour. Random distribution showed good fit at all locations under study with 95% confidence level. Normal distribution showed good fit at Loc. 1 and Loc. 2, whereas composite distribution fit only at Loc. 1. It was suggested that random distribution is to be used as an input in generating traffic in traffic analysis at highway sections where traffic volume are under 500 vehicles per hour.
Uav route planning for maximum target coveragecseij
Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of
the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the
targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of
UAVs to cover maximum number of targets considering their flight range.
The proposed solution for this practical optimization problem is designed by modifying the Max-Min Ant
System (MMAS) algorithm. To evaluate the success of the proposed method, an alternative approach,
based on the Nearest Neighbour (NN) heuristic, has been developed as well. The results showed the success
of the proposed MMAS method by increasing the number of covered targets compared to the solution based
on the NN heuristic.
Vehicle Headway Distribution Models on Two-Lane Two-Way Undivided RoadsAM Publications
The time headway between vehicles is an important flow characteristic that affects the safety, level of service, driver behavior, and capacity of a transportation system. The present study attempted to identify suitable probability distribution models for vehicle headways on 2-lane 2-way undivided (2/2 UD) road sections. Data was collected from three locations in the city of Semarang: Abdulrahman Saleh St. (Loc. 1), Taman Siswa St. (Loc. 2) and Lampersari St. (Loc.3). The vehicle headways were grouped into one-second interval. Three mathematical distributions were proposed: random (negative-exponential), normal, and composite, with vehicle headway as variable. The Kolmogorov-Smirnov test was used for testing the goodness of fit. Traffic flows at the selected locations were considered low, with traffic volume ranged between 400 to 670 vehicles per hour per lane. The traffic volume on Loc.1 was 484 vehicles per hour, that on Loc. 2 was 405 vehicles per hour, and that on Loc. 3 was 666 vehicles per hour. Random distribution showed good fit at all locations under study with 95% confidence level. Normal distribution showed good fit at Loc. 1 and Loc. 2, whereas composite distribution fit only at Loc. 1. It was suggested that random distribution is to be used as an input in generating traffic in traffic analysis at highway sections where traffic volume are under 500 vehicles per hour.
Adjusting the flow in crucial areas can maximize the overall throughput of traffic along a stretch of road. This is of particular interest in regions of high traffic density, which may be caused by high volume peak time traffic, accidents or closure of one or more lanes of the road.
RISK ANALYSIS FOR SEVERE TRAFFIC ACCIDENTS IN ROAD TUNNELS (PART I)Franco Bontempi
IF CRASC'15 - 14-16 MAGGIO 2015 ROMA
The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of a vehicle can degenerate into scenarios that can lead to a high number of victims. For example, on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine caught fire in the Mont Blanc Tunnel.
In the first part of this study has been summarized the operation logic of a specific model for the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated with a sensitivity analysis on specific parameters that have an influence on the risk.
In sections 2, 3, and 4 the concept of Risk and its assessment is dealt. In section 5, the proce-dure followed by the QRA model to derive societal and individual risk indicators is discussed, starting from a given number of possible accident scenarios. In section 6 conclusions are written regarding the application of the studied model.
RISK ANALYSIS FOR SEVERE TRAFFIC ACCIDENTS IN ROAD TUNNELS (PART II)Franco Bontempi
IF CRASC'15 - Roma, 14-16 maggio 2015.
The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of a vehicle can degenerate into scenarios that can lead to a high number of victims. For example, on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine caught fire in the Mont Blanc Tunnel.
In the first part of this study has been summarized the operation logic of a specific model for the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated with a sensitivity analysis on specific parameters that have an influence on the risk.
The section 2 of this paper describes the tunnel San Demetrio on which was carried out risk analysis applying the PIARC/OECD QRA model, and in the section 3 are reported the main analysis results. In section 4, conclusions regard to risk analysis applied to real case and about the sensitivity analysis are reported. In particular, the sensitivity analysis has highlighted the most influential parameters in the model.
Path Planning Algorithms for Unmanned Aerial Vehiclesijtsrd
In this paper, the shortest path for Unmanned Aerial Vehicles UAVs is calculated with two dimensional 2D path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment. The aim of this paper is to avoid obstacles and to find the shortest way to the target point. Th e simulation environment was created to evaluate the arrival time on the path planning algorithms A and Dijkstra algorithms for the UAVs. As a result, real time tests were performed with UAVs Elaf Jirjees Dhulkefl | Akif Durdu ""Path Planning Algorithms for Unmanned Aerial Vehicles"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23696.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23696/path-planning-algorithms-for-unmanned-aerial-vehicles/elaf-jirjees-dhulkefl
Adjusting the flow in crucial areas can maximize the overall throughput of traffic along a stretch of road. This is of particular interest in regions of high traffic density, which may be caused by high volume peak time traffic, accidents or closure of one or more lanes of the road.
RISK ANALYSIS FOR SEVERE TRAFFIC ACCIDENTS IN ROAD TUNNELS (PART I)Franco Bontempi
IF CRASC'15 - 14-16 MAGGIO 2015 ROMA
The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of a vehicle can degenerate into scenarios that can lead to a high number of victims. For example, on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine caught fire in the Mont Blanc Tunnel.
In the first part of this study has been summarized the operation logic of a specific model for the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated with a sensitivity analysis on specific parameters that have an influence on the risk.
In sections 2, 3, and 4 the concept of Risk and its assessment is dealt. In section 5, the proce-dure followed by the QRA model to derive societal and individual risk indicators is discussed, starting from a given number of possible accident scenarios. In section 6 conclusions are written regarding the application of the studied model.
RISK ANALYSIS FOR SEVERE TRAFFIC ACCIDENTS IN ROAD TUNNELS (PART II)Franco Bontempi
IF CRASC'15 - Roma, 14-16 maggio 2015.
The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of a vehicle can degenerate into scenarios that can lead to a high number of victims. For example, on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine caught fire in the Mont Blanc Tunnel.
In the first part of this study has been summarized the operation logic of a specific model for the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated with a sensitivity analysis on specific parameters that have an influence on the risk.
The section 2 of this paper describes the tunnel San Demetrio on which was carried out risk analysis applying the PIARC/OECD QRA model, and in the section 3 are reported the main analysis results. In section 4, conclusions regard to risk analysis applied to real case and about the sensitivity analysis are reported. In particular, the sensitivity analysis has highlighted the most influential parameters in the model.
Path Planning Algorithms for Unmanned Aerial Vehiclesijtsrd
In this paper, the shortest path for Unmanned Aerial Vehicles UAVs is calculated with two dimensional 2D path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment. The aim of this paper is to avoid obstacles and to find the shortest way to the target point. Th e simulation environment was created to evaluate the arrival time on the path planning algorithms A and Dijkstra algorithms for the UAVs. As a result, real time tests were performed with UAVs Elaf Jirjees Dhulkefl | Akif Durdu ""Path Planning Algorithms for Unmanned Aerial Vehicles"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23696.pdf
Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/23696/path-planning-algorithms-for-unmanned-aerial-vehicles/elaf-jirjees-dhulkefl
Airport Runway Detection Based On ANN AlgorithmIJTET Journal
Automatic detection of airports is especially essential, attributable to the strategic importance of those targets. during this paper, a detection methodology is planned for flying field runways. This methodology, that operates on massive optical satellite pictures, consists of a segmentation methodsupported textural properties, and a runway form detection stage. within the segmentation method, manynative textural optionsarea unit extracted. Since the most effective discriminative options for flying field runways cannot be trivially foreseen, the ANN algorithmic ruleis utilized as a feature selector over an oversized set of options. Moreover, the chosenoptions with corresponding weights willgivedata on the hidden characteristics of runways. The plannedalgorithmic rule is examined with experimental work employing a comprehensive knowledge set consisting of enormous and high resolution satellite pictures and thriving results area unit achieved.
Sampling based positioning of unmanned aerial vehicles as communication relay...Inkonova AB
In the last years, the use of Unmanned Aerial Vehicles (UAVs, also known as “drones”) have found application in different environments that are dangerous or inaccessible by humans like inspection or mapping of underground mining stopes or shafts. During a drone mission it is often required to maintain connectivity with the ground station (referred hereinafter as GS). Even in autonomous flights, real-time communication provides several advantages like active operator supervision and eventual mission correction, in-flight mapping data transfer in case of drone crash inside an inaccessible area and others. In this context, we are interested in using a drone “leader” to explore unknown, dangerous and/or inaccessible underground areas, while keeping constant communication with the GS.
In this paper, we address the problem of using a swarm of autonomous drones, “repeaters”, as a relay chain to maintain communication between a GS and the drone leader responsible for exploration and data acquisition. We propose a sampling-based solution for dynamical positioning of the relay chain. Our method is fully decentralized, scalable and can deal with the case when the trajectory of the main drone is unknown. Simulation results are provided to show the performance of the proposed algorithm.
To simulate the behavior of the relay chain, we use a 2D simulation environment where the trajectory of the leader is predefined but not provided to the repeaters. The model used for the drone’s motion is based on a control signal that is provided as an acceleration and velocity that are bounded, and the drone is modeled as a point in space without orientation (also known as “headless” or “head-free”). In trivial situations, our algorithm can position the relay chain from the current and past mapping data from the leader. Further exploration and analysis of the utility functions to evaluate the sampled positions could drastically improve the performance. A higher level coordination for the whole drone repeaters’ chain could be achieved by using Behavior Trees, which would also increase the robustness and reliability of the whole system.
The objective of path planning algorithms is to find the optimal path from a source position to a target position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The proposed approach does not identify any specific points outside or between obstacles to solve the problems of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle the problems resulting from generating points inside the obstacles, or the intersection between path segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes into account the intersections between the path segments and obstacles to find a collision-free and near to optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on multi-core processors using OpenMP. The execution time of the proposed parallel implementation is reduced compared to sequential execution.
UAV PATH PLANNING USING GENETIC ALGORITHMWITH PARALLEL IMPLEMENTATIONijcsity
The objective of path planning algorithms is to find the optimal path from a source position to a target
position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The
proposed approach does not identify any specific points outside or between obstacles to solve the problems
of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle
the problems resulting from generating points inside the obstacles, or the intersection between path
segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes
into account the intersections between the path segments and obstacles to find a collision-free and near to
optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential
implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic
Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on
multi-core processors using OpenMP. The execution time of the proposed parallel implementation is
reduced compared to sequential execution.
Performance Analysis of Adaptive DOA Estimation Algorithms For Mobile Applica...IJERA Editor
Spatial filtering for mobile communications has attracted a lot of attention over the last decade and is cur-rently considered a very promising technique that will help future cellular networks achieve their ambi-tious goals. One way to accomplish this is via array signal processing with algorithms which estimate the Direction-Of-Arrival (DOA) of the received waves from the mobile users. This paper evaluates the per-formance of a number of DOA estimation algorithms. In all cases a linear antenna array at the base station is assumed to be operating typical cellular environment.
Waypoint Flight Parameter Comparison of an Autonomous Uavijaia
The present paper compares the effect of different waypoint parameters on the flight performance of a
special autonomous indoor UAV (unmanned aerial vehicle) fusing ultrasonic, inertial, pressure and optical
sensors for 3D positioning and controlling. The investigated parameters are the acceptance threshold for
reaching a waypoint as well as the maximal waypoint step size or block size. The effect of these parameters
on the flight time and accuracy of the flight path is investigated. Therefore the paper addresses how the
acceptance threshold and step size influence the speed and accuracy of the autonomous flight and thus
influence the performance of the presented autonomous quadrocopter under real indoor navigation
circumstances. Furthermore the paper demonstrates a drawback of the standard potential field method for
navigation of such autonomous quadrocopters and points to an improvement
This paper proposes a method to calculate a flight cost of an unmanned aerial vehicle (UAV) considering its change of heading angle though there are many reasons that cause the energy consumption. The proposed approach demonstrates that when a UAV moves from a starting position/point to a target/goal position/point, if the number of obstacle increases, the number of heading change would also increase. As a result, it raises the energy consumption of the UAV. It also shows that the magnitude of heading change would affect the energy consumption proportionally. The theoretical analysis as well as the simulation outcome proves the usefulness of the proposed technique.
Path Loss Prediction by Robust Regression Methodsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Preference of Efficient Architectures for GF(p) Elliptic Curve Crypto Operati...CSCJournals
This paper explores architecture possibilities to utilize more than one multiplier to speedup the computation of GF(p) elliptic curve crypto systems. The architectures considers projective coordinates to reduce the GF(p) inversion complexity through additional multiplication operations. The study compares the standard projective coordinates (X/Z,Y/Z) with the Jacobian coordinates (X/Z2,Y/Z3) exploiting their multiplication operations parallelism. We assume using 2, 3, 4, and 5 parallel multipliers and accordingly choose the appropriate projective coordinate efficiently. The study proved that the Jacobian coordinates (X/Z2,Y/Z3) is preferred when single or two multipliers are used. Whenever 3 or 4 multipliers are available, the standard projective coordinates (X/Z,Y/Z) are favored. We found that designs with 5 multipliers have no benefit over the 4 multipliers because of the data dependency. These architectures study are particularly attractive for elliptic curve cryptosystems when hardware area optimization is the key concern.
Similar to Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA* (20)
Here are a few sample handmade responses from the second time students in this advanced English course on grammar have used handmade responses for drawing their responses to a reading assignment. In this case, chapter 3 of Constance Hale's Sin and Syntax on verbs.
The purpose of the handmade response is to promote reading engagement so that students will be prepared for class discussion of the assigned reading for the day.
.
On the first day of USTD 1101: Strategies of Learning, an 8 week college course designed to help students on academic probation obtain successful learning habits, I asked these 19 students to write for about 5 minutes on their thoughts, feelings, and attitudes about being on probation and having to take this course.
I then asked them to draw a picture that reflected in some way what they had written.
The purpose of this presentation is to share how I often use drawing in my classes to help me and my students to review their feelings toward the course topic.
In the slides that follow, I share how my students in an advanced course on English grammar depicted their initial feelings through drawing and how I intend for them to use those drawings in future assignments.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
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Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA*
1. The Journal of Grey System 1 (2011 ) 35-46 35
Multi-objective and Multi-constrained UAV Path
Plan Optimum Selection Based on GRA*
Hu Zhong-hua'*, Zhao Min', Yao Min', Zhang Ke^
1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics,
Nanjing, 210016, China
2. College of Economics and Management, Nanjing University ofAeronautics and Astronautics,
Nanjing, 210016, China
'Corresponding Author: E-mail: chzhualong@yahoo.com.cn
Received January 2010
Abstract — To solve two-dimensional route planning problems of the Unmanned Aerial
Vehicle (UAV), optimal decision-making system of UAV flight path is established. And optimal
mathematical model of UAV flight path is also constructed. Grey relational analysis method is
applied to deal with the gray relational information among the various indicators and to solve
the model. Finally, the optimal model is used to plan optimum seeking for flight path planning
problem with seventeen radar threat nodes, five missile threat nodes, ten artillery threat nodes
and two climate threat nodes. The flight path with the best overall performance and minimum
comprehensive cost was obtained and the research provides a theoretical basis for further study
of the three-dimensional UAV flight path optimization.
Keywords: Grey Relational Analysis (GRA); UAV; Path planning; Radar; Missile; Artillery.
Introduction
Unmanned Aerial Vehicle (UAV) path plan optimum selection is the design of a flight
path from take-off area to the target area, and it must consider the fuel consumption
and avoid threats of radars, missiles, artilleries and climate, and then minimize the
overall comprehensive cost. Flight path planning is the most important part of UAV
•This research is supported by the foundation of Aviation Science Fund (Project no: 2009ZC52041)
and National Natural Science Foundation (Project no: 60974104).
2. 36 Hu Zhong-hua et al
combat mission planning. Mission is carried out by flight path, so reasonable path
planning can enable UAV to avoid threats effectively and improve survival probability
and operational efficiency [l].When getting a flight path, we must consider flve cost
indicators, they are fuel cost, radar threat cost, missile threat cost, artillery threat cost
and climate threat cost. In addition, we must also consider the constraints of them, just
like the greatest impact distance and the effective distance of radar threat and missiles
threats. There are certain correlations between these threats (e.g. radars detection
results can play an important role for guide the missiles to attack the UAV). Therefore,
the UAV flight path planning is a multi-constrained and multi-objective optimization
decision-making system, and is also an organic whole, all these factors are interrelated
and affect the system features jointly, and the impact is difficult to determine. That is to
say, it is a gray information system. The gray information often contains the correlation
between each indicator, so it is an overall information system, and in the design of path
plan optimization decision-making process, these information should be made full use.
The traditional approach of direct weighted sum often does not reflect the gray
information of these indicators, so this paper introduces grey relation analysis (GRA)
[2,3] and experience evaluation method to build UAV path plan optimum selection
model. And the optimal model is used to plan optimum seeking for flight path planning
problem with seventeen radar threat nodes, flve missile threat nodes, ten artillery threat
nodes and two climate threat nodes.
Flight Path Plan Problem Description
Flight path space representation
Because UAV usually maintain the level and speed unchanged in the cruise phase, and
the enemy's defensive zone is also in the flat region, there is no need to consider threats
avoidance by the use of terrain. Flight path planning issues can be simplified to a
two-dimensional space and it is a multi-object and multi-constrained optimum
selection problem. Survivability probability and effectiveness of UAV in the process of
implementation combat missions must also be considered, so it is one kind of special
optimum selection problem [4]. The flying space is divided by rectemgular grid. Fight
path is constituted by a group in the node vector, from the current node to the next
adjacent node. Therefore, the data structure of it is a Lo Shu Square with the current
node as the center and has eight adjacent nodes. Figure 1 is the adjacent node map of
nodes / .The adjacent nodes in path must be also adjacent in space. The size of grid
3. Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA 37
must be set according to the actual scale of the space and the distribution of the threats
nodes.
Adjacent Tlodes
Fig. 1. Net-construction for node.
The indicator of UAV fíght path
The indicator of UAV fight path consists mainly of fuel cost and the threats cost. And
the threats cost includes radars, missiles, artilleries and climate threat, as shown in
formula (1). The goal of path plan optimum selection is to make the overall
comprehensive cost minimum. And there are some constraints such as the greatest
impact distance and the effective distance for radars, missiles, artilleries and climate
models; therefore, the issue is a multi-objective and multi-constrained plan optimum
selection problem [4].
(1)
In formula (1): s is the UAV fiight path, s' is the optimum plan; WD(S) is radar threat
cost of s, and Wu(.s) stands for missile threat cost, WA(S) stands for artillery threat cost
and Wc(,s) stimds for climate threat cost. Wois) is cost of fuel consumption. Fuel cost is
a function of the voyage, and other threats cost is relative with detection range of
radars and the radius of destruction of missiles, artilleries and climate. It can be
specifically calculated as follows.
Establishment of threats models
Radars, missiles, artilleries, and climate threat model, respectively, are defined as
follows [5]:
Radars detection probability for UAV can be described as:
(2)
4. 38 Hu Zhong-hua et al.
In formula (2), P/^did is the probability of radars threats, dR stands for the distance
between the UAV and the radars, ¿/smax stands for the radius of maximum detection of
radars. When exceeding the distance, the return signal is so weak, and will be drowned
in the noise. <R i is a radius of effective detection of radars. Within this range, the
Í mn
detection probability is one. P!^dR)=l indicates that the detection probability of UAV is
1, so the radar threat is infinity. Pa(dii)=O indicates that the detection probability is 0
and then the cost of radar threat is zero, and as between the two, the probability is
Destruction probability of missiles, artilleries and climate for UAV can be described
as follows:
In formula (3), (4) and formula (5), the destruction probability of missiles, artilleries
and climate are described respectively, just like in formula (2). But there are two
differences. One is that subscripts have different means. M means missiles, A means
artilleries and C means climate. And another difference is that it is 1 / d^ in formula
(2),while l/d,m formula (3)^ (4) and formula (5).
After the indicator functions of optimization selection are defined, the indicator cost
can be calculated for a given path respectively.
GRA for Plan Set of UAV Flight Path
UAV fiight path plan set is composed by the n plans '*'. Each plan has m indicators set.
In this paper, UAV plan has five indicators, namely, the cost of radars threats, missiles
threats, artilleries threats, climate threat and the cost of fuel consumption.
Path / with m indicators in gray system can be expressed as a vector x,.
x,=ixn, xa, ••• , Xi„) , / = l,2,---,n, j = l,2,---,m (6)
And then gray system with n Paths and m indicators for each path can be expressed as
5. Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA 39
a matrix X„y„ as follows:
Xy
y =
'•2«
(7)
In order to facilitate grey relation analysis, each evaluation indicator values for all
alternative UAV flight path plans are treated as non-dimensional standardized
indicators. The treatment methods are shown as follows:
Path plan indicators in this article are the cost indicators, therefore, smaller for
comprehensive cost, better for overall performance, and the standardized formula is
shown as follows [7]:
-Xy +max
(8)
In formula(8), / = l,2,---,n,y = 1,2,---,OT.
After normalized treatment, matrix A^^m becomes series r, and is shown as follows:
rrinura, •••,r,„), / = 1,2,••-,«. (9)
UAV path plan optimum Selection for n plans has a relative comparison with each
other. That is to the say the relative importance of m evaluation indicators must be
considered during optimum selection for the gray system, therefore an ideal reference
plan is determined, denoted as follows:
/2 ' • • • ' / ; . • • • ' / « ] (10)
In formula {0), f° =m2x{rj, r2j,---,r„j,),j = l,2,---.,m. That is to say m evaluation
indicators of f* are the maximum of the corresponding evaluation indicator for all n
alternative paths, and it is considered as the ideal path plan (the ideal solution) and as
the standard. The ideal path plan is a reference sequence and al! these n a!temative
paths are comparative sequences which are compared with reference sequence
respectively [8] .The approach degree between reference sequence and comparative
sequence is usually measured by grey incidence coefficient, (^¡j is the grey incidence
coefficient between indicator rjj of sequence comparison /-,, and f° of reference
sequence
6. 40 Hu Zhong-hua et al
mm mm / f - r-' I + p max max
i
in
•' j i -'¡'-
J i = ,2,--,m. (11)
In formula (11), p€[0,], generally take p=0.5. And then grey incidence coefficient
matrix for the plans set of UAV fiight path scheme can be shown as follows:
9 21
(12)
7n2
Solution for optimum selection model
Evaluation system of UAV path plan selection includes fiiel cost and the cost of radars
threats, missiles threats, artillery threat and climate threat. Assume that there are
«paths, expressed respectively as: Si^2,-",Sj,---,s„. Among them, the composition of
indicators for path J can be expressed by Xj vector :
And n paths constitute a set of alternative plans: X(x, JC2,---,x„). After quantifying the
various performance indicators, a reference indicator set is determined and it is
constituted by choosing the best indicator of the value of UAV fiight path plans [9-10].
Reference indicator set describes a reference design of UAV flight path, and it is the
ideal solution. And then grey relational coefficient matrix for n kinds of design options
can be obtained and they are relative to reference design. Grey relational coefficient
matrix is described as H :
R2 Ml A2 C2 02
(13)
In formula (13), ^ is the grey relational coefficient of evaluation indicators relative to
the reference indicators.
The weight of fuel consumption and the cost of radars threats, missiles threats,
artillery threat and climate threat are calculated by using experience evaluation method
7. Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA 41
and shown as:So, SR, SM, SA and SQ,. The grey relational degree R{ru r2,-",r„)^of each
plan's decision-makers can be calculated as follows:
»fil Wfl Í4I ici 501
R can be sorted according to size, and the best one is the largest one and its plan is the
optimum plan s*, its corresponding indicators is are the optimum indicators: x*.
Examples of Path Plan Optimum Selection
In this paper. The UAV flight path parameters include UAV takeoff location
coordinates, the destination location coordinates and the coordinates for seventeen
missiles threats, ten artilleries threats, flve air missile threats and two climate threats,
as shown in Table 1 [11]. The entire flight path maps were drawn by using Matlab.
Tablel. Menace nodes, start node and destination node.
Start node (10,20) Destination node (40, 50)
No. (x,y) No. (x,y) No. (x,y)
1# (17,60) 7# (22,28) 3# (26,55)
2# (32,66.5) 8# (45,30) 14# (47,49)
Radar threat 3# (50,62) 9# (32,22) 15# (24,42)
nodes 4# (57,45) 10# (36,32) 16# (33,54)
5# (51.5,31) 11# (12,36) 17# (37,55)
6# (35,26) 12# (11,48)
1# (17,22) 4# (46, 54)
Missile threat
nodes 2# (40,62) 5# (56, 38)
3# (26, 30)
1# (14,46) 5# (26,22) 9# (20,30)
Artillery 2# (37,47) 6# (35,37) I0# (32,34)
threat nodes 3# (10,30) 7# (30,35)
4# (34,50) 8# (30,50)
Climate 1# (16,40)
threat nodes 2# (24,48)
The threats model parameters of radars, missiles, artilleries and atmospheric were set
to: dRmiB=4, £/ßmax=80, í4/min=4, <4/max=60, í/xinin=3, £(<niax=15, £¿Cmin=2, öti«ax=8. Their
weights were calculated by experience evaluation method.
8. 42 Hu Zhong-hua et al
Alternative plan set of UAV flight path
On the bases of meeting the threats constraints of radars, artilleries, missiles and
climate, thirty UAV fiight paths are determined as alternatives by identifying regions of
random search algorithm. Figures from Fig.2 - Fig.31 describe the thirty alternative
fiight path maps respectively.
o ••• "'
o o O O
o o o o o « o o
. " , Î »
'¿.3 Plan 2 llight path J.4 Plan 3 flighlpath
I
i ig.5 Plan 4 tlight path i- ig.ojr^lan :J iiigni patn r íg.b t'lan i iiignt pam
o o
o o o o
Fig. 8 Plan 7 flight paüi_^ Fig.9 Plan 8 flight path Fig. 10 Plan 9 flight path
o o o o o o
o o » o
itp- • ' . ^ <^
Fig 11 Plan 10 flight path Fig. 12 Plan 11 flight path Fig. 13 Plan 12 flight piith
o o
o « o o
;3nightpath í ig. 15 Plan 14 flight path Fig.lóPlanl;
o o o o o o
« o o o
3 ^
<» ..T^^*.
Fig. 17 Plan 16 flight path Fig. 18 Plan 17 flight path Fig. 19 Plan 18 flight path
9. Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA 43
o o o o
o o o
-' r o -»o
Sí—*— « .Ï
Fig.2O Plan 19 flight path Fig.21 Plaii 20 flight patl. Ig 22 pían iîfïiglu paili
O o
I-I
:Mg.24 Plan 23 flight path F i g . 2 5 r ,,;¡i . , i¡, , ;•;
O O ~ o o
o o
íí ¡e Ís-"-i'¿ P.
;.26 Plan 25 flight path Fig.27 Plan 26 flight patli Fig.28 Pian 27 íligiu JÚU
o"' i "a 1 (5 - —x._
o »
Hg.2y Plan 2Ü llight path Fig.3O Plan 29 tliglit paüi Fig.31 Plan 30 fliglit path
In these figures, square stands for the path starting node, and five-pointed stands for
the track nodes, and solid circle stands for the target nodes, and diamond stands for
radar threat nodes, and the triangle stands for the anti-aircraft artillery threat nodes, and
hexagonal stands for climate threat nodes, and hollow-point circle stands for the
missile threat nodes.
UAV flight path optimum selection
According to the formula (2-5), the thirty UAV flight path of the cost indicators are
calculated through the Matlab programs, the results are shown in Table 2. According to
the formula (8) and combining with the data of Table 2, the normalized values of each
indicator and reference normalized values are calculated. According to the formula (11)
the grey incidence coefficient for each indicator of every path plan are calculated. The
results are shown in Table 3. And they are compared with the ideal solution. These
indicators include the fuel consumption cost, the radar threat cost, artillery threat cost,
missile threat cost and climate threat cost.
10. 44 Hu Zhong-hua et al
Table 2.The initial value of each indicator.
Plan No. Fuel cost Radars cost Artilleries cost Missiles cost climate cost
1 26 0.0228 16.0365 7.4902 0.3349
2 23 0.0206 13.9257 6.7246 0.4355
3 23 0.0208 14.2023 6.8994 0.2968
4 22 0.0213 13.2377 6.3322 0.3349
5 22 0.0212 13.4521 6.3458 0.2968
6 22 0.0179 13.3222 6.3981 0.2968
7 23 0.0243 14.3953 6.7374 0.5936
8 22 0.0228 13.8197 6.2994 0.4930
9 23 0.0207 14.4064 6.8695 0.3349
10 22 0.0169 13.3413 6.5782 0.3349
11 21 0.0176 13.1398 6.3902 0.3349
12 21 0.0159 13.2410 6.2513 0.3349
13 21 0.0162 13.1843 6.1421 0.2968
14 25 0.0198 15.3825 7.5423 0.2968
15 22 0.0173 13.7966 6.5797 0.2968
16 21 0.0170 13.0862 6.2336 0.2968
17 21 0.0163 13.4766 6.2487 0.2968
18 26 0.0234 15.4881 7.3499 0.4736
19 23 0.0226 13.9014 6.8143 0.4355
20 23 0.0180 14.4413 6.8487 0.4355
21 23 0.0217 14.6534 6.8650 0.3349
22 21 0.0201 13.3901 6.2239 0.4355
23 22 0.0168 13.6482 6.6807 0.3349
24 24 0.0225 14.5526 6.9668 0.4355
25 22 0.0221 13.7040 6.5854 0.2968
26 22 0.0221 13.7040 6.5854 0.2968
27 27 0.0212 16.5315 7.9282 0.3349
28 23 0.0215 14.4353 6.9410 0.2968
29 21 0.0215 13.1435 6.1094 0.4930
30 22 0.0198 14.1398 6.6100 0.2968
First, the weight of each indicator was obtained by using experience evaluation method,
the result was respectively <5b=0.2, 4=0.1, ^^=0.3, 5^f^Çi.2 and ^ 0 . 2 . Then,
according to formula (14), the grey incidence degree R was calculated, and it was the
comprehensive performance of the each plan relative to the reference indicator of
reference plan (the ideal solution).And the result was i?=(0.4620, 0.5916, 0.6552,
0.7893, 0.8004, 0.8334, 0.5088, 0.6498, 0.6046, 0.7832, 0.8741, 0.9074, 0.9702,
0.5438, 0.7692, 0.9552, 0.9093, 0.4121, 0.5782, 0.5683, 0.5875, 0.7860, 0.7405,
0.5073, 0.7425, 0.7425, 0.4367, 0.6356, 0.8193, 0.7170). Where, the value of
comprehensive evaluation for the 13th plan was the maximum and the value was
0.9702. Therefore, the best solution for each path plan was the 13th plan. That was to
11. Multi-objective and Multi-constrained UAV Path Plan Optimum Selection Based on GRA 45
say, the optimal solution for the UAV path plan optimum seeking model was x*=(2I,
0.0162, 13.1843, 6.1421, 0.2968), so the optimal path s* was the 13th path (Fig.l4) and
its overall cost was minimal.
Table 3. Grey relational coefficient of each indicator.
Plan No. Fuel cost Radars cost Artilleries cost Missiles cost climate cost
1 0.3750 0.3784 0.3686 0.3971 0.7957
2 0.6000 0.4719 0.6723 0.5965 0.5169
3 0.6000 0.4615 0.6068 0.5351 1.0000
4 0.7500 0.4375 0.9192 0.8032 0.7957
5 0.7500 0.4421 0.8248 0.7937 1.0000
6 0.7500 0.6774 0.8795 0.7590 1.0000
7 0.6000 0.3333 0.5682 0.5915 0.3333
8 0.7500 0.3784 0.7014 0.8272 0.4306
9 0.6000 0.4667 0.5661 0.5447 0.7957
10 0.7500 0.8077 0.8710 0.6598 0.7957
11 1.0000 0.7119 0.9698 0.7641 0.7957
12 1.0000 1.0000 0.9175 0.8650 0.7957
13 1.0000 0.9333 0.9461 0.9653 1.0000
14 0.4286 0.5185 0.4286 0.3883 1.0000
15 0.7500 0.7500 0.7080 0.6591 1.0000
16 1.0000 0.7925 1.0000 0.8798 1.0000
17 1.0000 0.9130 0.8152 0.8672 1.0000
18 0.3750 0.3590 0.4177 0.4230 0.4563
19 0.6000 0.3853 0.6788 0.5633 0.5169
20 0.6000 0.6667 0.5597 0.5516 0.5169
21 0.6000 0.4200 0.5236 0.5462 0.7957
22 1.0000 0.5000 0.8500 0.8882 0.5169
23 0.7500 0.8235 0.7540 0.6142 0.7957
24 0.5000 0.3889 0.5402 0.5147 0.5169
25 0.7500 0.4038 0.7360 0.6564 1.0000
26 0.7500 0.4038 0.7360 0.6564 1.0000
27 0.3333 0.4421 0.3333 0.3333 0.7957
28 0.6000 0.4286 0.5608 0.5223 1.0000
29 1.0000 0.4286 0.9678 1.0000 0.4306
30 0.7500 0.5185 0.6205 0.6450 1.0000
Conclusions
To solve the problem of multi-objective and multi-constrained UAV path plan optimum
seeking, this paper established the threats model of UAV fiight path plan and its goal
system of optimizing and decision-making, including five decision-making objectives,
such as fuel cost and the cost of radars threats, missiles threats, artillery threat and
12. 46 Hu Zhong-hua et al.
climate threat. The constraints for the greatest impact distance and the effective
distance of these threats models are introduced into cost function. And by this way,
path optimization selection mathematical model of UAV flight path is established.
Then GRA method is used to solve the model. At last, the optimization model is
applied to real path optimization selection problem with seventeen radar threat nodes,
flve missile threat nodes, ten artillery threat nodes and two climate threat nodes. The
path with best comprehensive performance (minimum comprehensive cost) is sought
by the method. The method can avoid the subjectivity and randomness of traditional
selection and provide a theoretical basis for further study three-dimensional
multi-objective and multi-constrained UAV path plan optimum selection.
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