2D path planning for UAVs in radar threatening environment using simulated annealing algorithm

Turker, Tolgahan; Şahingöz, Özgür Koray; Yılmaz, Güray

doi:10.1109/icuas.2015.7152275

Cited by 42 publications

(20 citation statements)

References 11 publications

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“…This algorithm can also be used to avoid regular circular radar threats [10]. It provides satisfactory answers and results with the ability of escaping from the local minima by using capital agreement rule and the threat that proposed avoidance approach used to the excellent found answers making the threat-free path easily.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…The purposed system is written in the Java language with a Graphical User Interface (GUI) to display the results. In paper [10], the author explains Simulated Annealing (SA) Algorithm used for optimizing the 2D path for UAVs. The paper discusses about the easy threat avoidance methods and applies it to the clarification using SA to bypass from the legitimate circular radar hazard.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The paper discusses about the easy threat avoidance methods and applies it to results using SA to avoid regular circular radar hazards [10].…”

mentioning

confidence: 99%

See 2 more Smart Citations

An Overview of Classification, Requirements, Path Planning Algorithms and Improvement Areas of Unmanned Aerial Vehicle

Sharma¹,

Singh²,

Tayal³

2018

IJCA

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The need of Unmanned Aerial Systems (UASs) is expanding day by day as it can be used in both public and military environments. As the need for UAV is growing, there is an expansion inthe requirement for more reliable, authentic, efficient, optimized and strong vehicles that are capable for executing various operations. The need of such systems is mainly by the Militaries that continue to desire more UAV functionalities for diverse operations and tasks that can be performed all over the world. To have a continuous advancement in the field of autonomous UAV control system many cogent research works has been performed. A large amount of work is focused on the subsets of UAS control such as path planning algorithms, control of small UAV and autonomy. As various markets are amplifying, the necessity to have such systems with capability to adapt according to introduced tasks, sensing elements, and surroundings will drive requirements. They can be used in several ways in various models, sizes and types according to the needs of various operations. The most common Unmanned Aerial Vehicle is "drone"that can be operated by remote controllers using radio waves. These UAVs normally contains internal memory and uses battery power as a means to an energy source. In the current scenario, commercial drones are normally subjected to manual control by human that can control only one drone at a point of time. In accordance to set up a controlling system of multiple drones, collision avoidance mechanism and the detection of objects around these UAV systems is very necessary. This paper discusses about the UAVs requirements and capabilities along with path planning algorithms. It also provides some problems associated with a UAV system along with its improvement areas. KeywordsUnmanned Aerial System (UAS),autonomous systems, Unmanned Aerial Vehicle(UAV), Drone, path planning algorithms.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An Overview of Classification, Requirements, Path Planning Algorithms and Improvement Areas of Unmanned Aerial Vehicle

Sharma¹,

Singh²,

Tayal³

2018

IJCA

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“…A cooling schedule is used to determine when the temperature has been sufficiently cooled from the initial value. Turker et al [57] presented a method for 2D path planning in a radar threat constrained environment using a simulated annealing algorithm. Leary et al [26] evaluated five algorithms including SA, Consensus Based Bundle Algorithm (CBBA), greedy allocation, optimal Mixed Integer Linear Programming (MILP), and suboptimal MILP.…”

Section: Fixed Targetmentioning

confidence: 99%

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

2016

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Abstract:The use of unmanned aerial systems (UASs) in both the public and military environments is predicted to grow significantly. As the demand for UASs grows, the availability of more robust and capable vehicles that can perform multiple mission types will be needed. In the public sector, the demand will grow for UASs to be used for agriculture, forestry, and search and rescue missions. Militaries continue to demand more UAS capabilities for diverse operations around the world. Significant research has been performed and continues to progress in the areas of autonomous UAS control. A majority of the work focuses on subsets of UAS control: path planning, autonomy, small UAS controls, and sensors. Minimal work exists on a system-level problem of multiple-scenario UAS control for integrated systems. This paper provides a high-level modular system architecture definition that is modifiable across platform types and mission requirements. A review of the current research and employment of UAS capabilities is provided to evaluate the state of the capabilities required to enable the proposed architecture.

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“…In another case, the K-means algorithm and SA algorithm were combined for problems with multiple UAVs and multiple missions under complicated constraints [ 22 ]. 2D path planning in application scenarios with radar threatening was also studied in [ 23 ], in which SA algorithm was adopted to obtain nearly optimal path in 2D constrained environment, the author also tried to solve the route planning problems for multiple UAVs by executing parallel SA algorithms in [ 24 ], and demonstrated its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

A Novel Simulated Annealing Based Strategy for Balanced UAV Task Assignment and Path Planning

Huo

Zhu

et al. 2020

Sensors

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The unmanned aerial vehicle (UAV) has drawn increasing attention in recent years, especially in executing tasks such as natural disaster rescue and detection, and battlefield cooperative operations. Task assignment and path planning for multiple UAVs in the above scenarios are essential for successful mission execution. But, effectively balancing tasks to better excavate the potential of UAVs remains a challenge, as well as efficiently generating feasible solutions from the current one in constrained explosive solution spaces with the increase in the scale of optimization problems. This paper proposes an efficient approach for task assignment and path planning with the objective of balancing the tasks among UAVs and achieving satisfactory temporal resolutions. To be specific, we add virtual nodes according to the number of UAVs to the original model of the vehicle routing problem (VRP), thus make it easier to form a solution suitable for heuristic algorithms. Besides, the concept of the universal distance matrix is proposed to transform the temporal constraints to spatial constraints and simplify the programming model. Then, a Swap-and-Judge Simulated Annealing (SJSA) algorithm is therefore proposed to improve the efficiency of generating feasible neighboring solutions. Extensive experimental and comparative studies on different scenarios demonstrate the efficiency of the proposed algorithm compared with the exact algorithm and meta-heuristic algorithms. The results also inspire us about the characteristics of a population-based algorithm in solving combinatorial discrete optimization problems.

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2D path planning for UAVs in radar threatening environment using simulated annealing algorithm

Cited by 42 publications

References 11 publications

An Overview of Classification, Requirements, Path Planning Algorithms and Improvement Areas of Unmanned Aerial Vehicle

An Overview of Classification, Requirements, Path Planning Algorithms and Improvement Areas of Unmanned Aerial Vehicle

Multiple-Scenario Unmanned Aerial System Control: A Systems Engineering Approach and Review of Existing Control Methods

A Novel Simulated Annealing Based Strategy for Balanced UAV Task Assignment and Path Planning

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