scholarly journals Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas

2015 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Risk Assessment ◽  
Path Planning ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

Risk-Based Path Planning Optimization Methods for Unmanned Aerial Vehicles Over Inhabited Areas1

2016 ◽  
Vol 16 (2) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality ◽  
Aerial Vehicles

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches that can find low-risk flight paths between the mission's start and finish points. Because the flight paths with the lowest risk could be excessively long and indirect, UAV operators are concerned about the tradeoff between risk and flight time. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

2020 ◽  
Author(s):  
Rui Qiu ◽  
Yongtu Liang
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Flight Path ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Two Stage ◽  
Calculation Time ◽  
Pipeline Network ◽  
Second Stage ◽  
Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

scholarly journals A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

2021 ◽  
Vol 9 (11) ◽  
pp. 1243
Author(s):  
Charis Ntakolia ◽  
Dimitrios V. Lyridis
Keyword(s):  
Fuzzy Logic ◽  
Path Planning ◽  
Swarm Intelligence ◽  
Optimal Path ◽  
Computation Time ◽  
Computational Effort ◽  
Unmanned Vehicles ◽  
Maritime Transportation ◽  
Solution Quality ◽  
Multi Objective

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.

scholarly journals Global optimization of default phases for parallel transmit coils for ultra-high-field cardiac MRI

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255341
Author(s):  
Maxim Terekhov ◽  
Ibrahim A. Elabyad ◽  
Laura M. Schreiber
Keyword(s):  
Cardiac Mri ◽  
Computation Time ◽  
Optimization Methods ◽  
Fine Tuning ◽  
Optimization Approach ◽  
Brute Force ◽  
Global Extremum ◽  
High Magnetic Field Strength ◽  
Brute Force Method ◽  
Key Steps

The development of novel multiple-element transmit-receive arrays is an essential factor for improving B1+ field homogeneity in cardiac MRI at ultra-high magnetic field strength (B0 > = 7.0T). One of the key steps in the design and fine-tuning of such arrays during the development process is finding the default driving phases for individual coil elements providing the best possible homogeneity of the combined B1+-field that is achievable without (or before) subject-specific B1+-adjustment in the scanner. This task is often solved by time-consuming (brute-force) or by limited efficiency optimization methods. In this work, we propose a robust technique to find phase vectors providing optimization of the B1-homogeneity in the default setup of multiple-element transceiver arrays. The key point of the described method is the pre-selection of starting vectors for the iterative solver-based search to maximize the probability of finding a global extremum for a cost function optimizing the homogeneity of a shaped B1+-field. This strategy allows for (i) drastic reduction of the computation time in comparison to a brute-force method and (ii) finding phase vectors providing a combined B1+-field with homogeneity characteristics superior to the one provided by the random-multi-start optimization approach. The method was efficiently used for optimizing the default phase settings in the in-house-built 8Tx/16Rx arrays designed for cMRI in pigs at 7T.

scholarly journals Optimization techniques for economic load dispatch problem in power plant

2021 ◽  
pp. 10-17
Keyword(s):  
Computation Time ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Hybrid Techniques ◽  
Power Stations ◽  
Programming Techniques ◽  
Single Method ◽  
Parameter Robustness

This paper presents survey of optimization techniques used to solve the problem of economic load dispatch in power stations. Since there is no single method available for solving all economic dispatch problems efficiently, thus a number of different optimization methods have been developed to solve this problem. These techniques were divided into three types depending on the efficiency of the solution: stochastic process techniques, statistical methods, and mathematical programming techniques which divided to local optimization, and global optimization. It is found that is better to use hybrid techniques to overcome load dispatch problems so as to achieve significant improvements in computation time, convergence properties, solution quality, or parameter robustness.

Metaheuristic Optimization of a Discrete Array of Radiant Heaters

2006 ◽  
Vol 128 (10) ◽  
pp. 1031-1040 ◽  
Author(s):  
Jason M. Porter ◽  
Marvin E. Larsen ◽  
J. Wesley Barnes ◽  
John R. Howell
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Search Algorithm ◽  
Radiation Heat Transfer ◽  
Computation Time ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Metaheuristic Optimization ◽  
Design Variables ◽  
Radiant Heater

The design of radiant enclosures is an active area of research in radiation heat transfer. When design variables are discrete such as for radiant heater arrays with on-off control of individual heaters, current methods of design optimization fail. This paper reports the development of a metaheuristic thermal radiation optimization approach. Two metaheuristic optimization methods are explored: simulated annealing and tabu search. Both approaches are applied to a combinatorial radiant enclosure design problem. Configuration factors are used to develop a dynamic neighborhood for the tabu search algorithm. Results are presented from the combinatorial optimization problem. Tabu search with a problem specific dynamic neighborhood definition is shown to find better solutions than the benchmark simulated annealing approach in less computation time.

Optimal Power Control of Smart Grid Based on Neural Network Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 626-632
Author(s):  
Li Yan Zhang ◽  
Shu Hai Quan
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Smart Grid ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Approach ◽  
Neural Network Optimization ◽  
Control Approach ◽  
Optimal Power ◽  
Control Layer

This paper presents a neural network based optimal control approach for the power allocation problem of smart grid. Firstly the optimal control framework which consists of center control layer, area control layer and generator control layer is presented. In order to reduce computation time of optimal control, the neural network optimization method and schematic diagram is proposed. Simulation is implemented in a modified IEEE30-bus power system comprising 6 conventional generators and a wind plant. Simulation results demonstrate that the proposed neural network optimization approach is satisfied the demand of computation time and the optimal power controller can allocate the power into loads reasonably and cope with the fluctuation of the load and renewable energy source’s output power.

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

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