Decomposition of Collaborative Surveillance Tasks for Execution in Marine Environments by a Team of Unmanned Surface Vehicles

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Shaurya Shriyam ◽  
Brual C. Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Region Of Interest ◽  
Surface Currents ◽  
Environmental Constraints ◽  
Swarm Optimization ◽  
Speed Limits ◽  
Unmanned Surface Vehicles ◽  
Occupancy Grid ◽  
Optimal Region ◽  
Speed Restrictions ◽  
Effective Distribution

This paper introduces an approach for decomposing exploration tasks among multiple unmanned surface vehicles (USVs) in congested regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environmental constraints on the USVs. The performance of a USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths due to tides and weather. The team of USVs needs to explore a certain region of the harbor and we need to develop an algorithm to decompose the region of interest into multiple subregions. The algorithm overlays a two-dimensional grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, the rate at which each USV is able to travel varies with the applicable speed limits at the location. The objective is to minimize the time taken for the last USV to finish exploring the assigned area. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments where speed restrictions are not known in advance.

On-Line Task Decomposition for Collaborative Surveillance of Marine Environment by a Team of Unmanned Surface Vehicles

2017 ◽  
Author(s):  
Shaurya Shriyam ◽  
Brual C. Shah ◽  
Satyandra K. Gupta
Keyword(s):  
Maximum Velocity ◽  
Region Of Interest ◽  
Surface Currents ◽  
Time Step ◽  
Physical Constraints ◽  
Unmanned Surface Vehicles ◽  
Optimal Region ◽  
On Line ◽  
The Given ◽  
Effective Distribution

In this paper, we introduce an approach for decomposing exploration tasks among multiple Unmanned Surface Vehicles (USVs) in port regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environment on the physical constraints of the USVs. The performance of the USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths as a result of tides, and weather. In our approach, we want the team of USVs to explore certain region of the harbor. The algorithm has to decompose the region of interest into multiple sub-regions by considering the maximum operating velocity of each USV in the given environmental conditions. The algorithm overlays a 2D grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, each USV covers the maximum area that is possible by operating at maximum velocity at each time-step. The objective is to minimize the time taken for the last USV to finish claiming its area exploration. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments with unknown velocity profiles.

scholarly journals Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3096 ◽  
Author(s):  
Junfeng Xin ◽  
Shixin Li ◽  
Jinlu Sheng ◽  
Yongbo Zhang ◽  
Ying Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Sensor Fusion ◽  
Autonomous Navigation ◽  
Particle Swarm ◽  
Sensor Data ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Unmanned Surface Vehicles ◽  
Inertia Weight

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

scholarly journals Defining the Optimal Region of Interest for Hyperemia Grading in the Bulbar Conjunctiva

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
María Luisa Sánchez Brea ◽  
Noelia Barreira Rodríguez ◽  
Antonio Mosquera González ◽  
Katharine Evans ◽  
Hugo Pena-Verdeal
Keyword(s):  
Region Of Interest ◽  
Bulbar Conjunctiva ◽  
Ocular Diseases ◽  
Conjunctival Hyperemia ◽  
Limited Region ◽  
Computer Aided ◽  
Optimal Region ◽  
Regression Techniques ◽  
Grading Scale ◽  
Conjunctival Redness

Conjunctival hyperemia or conjunctival redness is a symptom that can be associated with a broad group of ocular diseases. Its levels of severity are represented by standard photographic charts that are visually compared with the patient’s eye. This way, the hyperemia diagnosis becomes a nonrepeatable task that depends on the experience of the grader. To solve this problem, we have proposed a computer-aided methodology that comprises three main stages: the segmentation of the conjunctiva, the extraction of features in this region based on colour and the presence of blood vessels, and, finally, the transformation of these features into grading scale values by means of regression techniques. However, the conjunctival segmentation can be slightly inaccurate mainly due to illumination issues. In this work, we analyse the relevance of different features with respect to their location within the conjunctiva in order to delimit a reliable region of interest for the grading. The results show that the automatic procedure behaves like an expert using only a limited region of interest within the conjunctiva.

Avoiding Increased Trip Times and Other Operational Impacts When Implementing Positive Train Control

2010 ◽  
Author(s):  
Davis Dure
Keyword(s):  
Speed Limits ◽  
Transit Systems ◽  
Speed Reduction ◽  
Train Control ◽  
Trip Time ◽  
Positive Train Control ◽  
Cab Signal ◽  
Operational Impacts ◽  
Speed Enforcement ◽  
Speed Restrictions

Implementing safety systems on railroads and transit systems to prevent collisions and the risks of excess speeds often come at the price of lengthened trip time, reduced capacity, or both. This paper will recommend a method for designing Positive Train Control (PTC) systems to avoid the degradation of operating speeds, trip times and line capacities which is a frequent by product of train-control systems. One of the more significant operational impacts of PTC is expected to be similar to the impacts of enforcing civil speed restrictions by cab signaling, which is that the safe-braking rate used for signal-system design and which is expected to be used for PTC is significantly more conservative than the service brake rate of the train equipment and the deceleration rate used by train operators. This means that the enforced braking and speed reduction for any given curve speed restriction is initiated sooner than it otherwise would be by a human train operator, resulting in trains beginning to slow and/or reaching the target speed well in advance of where they would absent enforcement. This results in increased trip time, which can decrease track capacity. Another impact of speed enforcement is that it often results in “underspeeding.” In a cab-signal (and manual-train-operation) environment, it has been well documented that train operators typically operate two or three mph below the nominal enforced speed to avoid the risk of penalty brake applications. Target and location speed enforcement under PTC is likely to foster the same behaviors unless the design is prepared to mitigate this phenomenon. While the trip-time and capacity impacts of earlier braking and train-operator underspeeding are generally marginal, that margin can be very significant in terms of incremental capacity and/or resource for recovery from minor perturbations (aka system reliability). The operational and design methodology that is discussed in this paper involves the use of a higher unbalance (cant deficiency) for calculating the safety speed of each curve that is to be enforced by PTC, while retaining the existing maximum unbalance standard and existing speed limits as “timetable speed restrictions”. Train operators will continue to be held responsible for observing the timetable speed limits, while the PTC system would stand ready to enforce the higher safety speeds and unbalance should the train operator fail to properly control his or her train. The paper will present a potential methodology for calculating safety speeds that are in excess of the normal operating speeds. The paper will also calculate using TPC software the trip-time tradeoffs for using or not using this potential concept, for which there are some significant precedents. Other operational impacts, and proposed remedies, will be discussed as well. These will include the issues of total speed enforcement versus safety-speed enforcement, the ability of a railroad’s management to perform the speed checks required by the FRA regulations under normal conditions, and the operation of trains under occasional but expected PTC failures.

scholarly journals The State of Art in Particle Swarm Optimization Based Unit Commitment: A Review

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 733 ◽  
Author(s):  
Gad Shaari ◽  
Neyre Tekbiyik-Ersoy ◽  
Mustafa Dagbasi
Keyword(s):  
Particle Swarm Optimization ◽  
Unit Commitment ◽  
State Of The Art ◽  
Cost Minimization ◽  
Particle Swarm ◽  
Environmental Constraints ◽  
Thermal Unit ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Modified Pso

Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also developed in literature to improve the convergence. In this study, these methods are grouped as extended PSO, modified PSO, and PSO with other techniques. This study shows that PSO with other techniques are utilized more than any other methods. In terms of constraints, it was observed that there are only few studies that considered Transmission Line (TL), Fuel (F), Emission (E), Storage (St) and Crew (Cr) constraints, while Power Balance (PB), Generation limit (GL), Unit minimum Up or Down Time (U/DT), Ramp Up or Ramp Down Time (R-U/DT) and system Spinning Reserve (SR) were the most utilized constraints in UC problems considering wind/solar as a renewable source. In addition, most of the studies are based on a single objective function (cost minimization) and, few of them are multi-objective (cost and emission minimization) based studies.

Optimal Region-of-Interest Settings for Tissue Characterization Based on Ultrasonic Elasticity Imaging

2008 ◽  
Vol 34 (4) ◽  
pp. 573-585 ◽  
Author(s):  
Kentaro Tsuzuki ◽  
Hideyuki Hasegawa ◽  
Masataka Ichiki ◽  
Fumiaki Tezuka ◽  
Hiroshi Kanai
Keyword(s):  
Tissue Characterization ◽  
Region Of Interest ◽  
Elasticity Imaging ◽  
Optimal Region ◽  
Ultrasonic Elasticity

scholarly journals From an Optimal Point to an Optimal Region: A Novel Methodology for Optimization of Multimodal Constrained Problems and a Novel Constrained Sliding Particle Swarm Optimization Strategy

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1808
Author(s):  
Carine M. Rebello ◽  
Márcio A. F. Martins ◽  
José M. Loureiro ◽  
Alírio E. Rodrigues ◽  
Ana M. Ribeiro ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Chemical Engineering ◽  
Particle Swarm ◽  
Confidence Regions ◽  
Local Minima ◽  
Optimal Point ◽  
Swarm Optimization ◽  
Deterministic Optimization ◽  
Optimal Region

The present work proposes a novel methodology for an optimization procedure extending the optimal point to an optimal area based on an uncertainty map of deterministic optimization. To do so, this work proposes the deductions of a likelihood-based test to draw confidence regions of population-based optimizations. A novel Constrained Sliding Particle Swarm Optimization algorithm is also proposed that can cope with the optimization procedures characterized by multi-local minima. There are two open issues in the optimization literature, uncertainty analysis of the deterministic optimization and application of meta-heuristic algorithms to solve multi-local minima problems. The proposed methodology was evaluated in a series of five benchmark tests. The results demonstrated that the methodology is able to identify all the local minima and the global one, if any. Moreover, it was able to draw the confidence regions of all minima found by the optimization algorithm, hence, extending the optimal point to an optimal region. Moreover, providing the set of decision variables that can give an optimal value, with statistical confidence. Finally, the methodology is evaluated to address a case study from chemical engineering; the optimization of a complex multifunctional process where separation and reaction are processed simultaneously, a true moving bed reactor. The method was able to efficiently identify the two possible optimal operating regions of this process. Therefore, proving the practical application of this methodology.

scholarly journals Tweeting about twenty: an analysis of interest, public sentiments and opinion about 20mph speed restrictions in two UK cities

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tushar Semwal ◽  
Karen Milton ◽  
Ruth Jepson ◽  
Michael P. Kelly
Keyword(s):  
Public Health ◽  
Public Opinion ◽  
Public Attitudes ◽  
Scale Up ◽  
Speed Limit ◽  
Speed Limits ◽  
The Public ◽  
Twitter Data ◽  
Potential Benefits ◽  
Speed Restrictions

Abstract Background Twenty miles per hour (20mph) speed limits (equivalent to roughly 30kmh) have become part of public health policies to reduce urban road collisions and casualties, especially in Western countries. Public opinion plays a crucial role in opposition to and acceptance of policies that are advocated for improving public health. Twenty miles per hour speed limit policies were implemented in Edinburgh and Belfast from 2016 to 2018. In this paper, we extract public opinion and sentiments expressed about the new 20mph speed limits in those cities using publicly available Twitter data. Methods We analysed public sentiments from Twitter data and classified the public comments in plain English into the categories ‘positive’, ‘neutral’, and ‘negative’. We also explored the frequency and sources of the tweets. Results The total volume of tweets was higher for Edinburgh than for Belfast, but the volume of tweets followed a similar pattern, peaking around 2016, which is when the schemes were implemented. Overall, the tone of the tweets was positive or neutral towards the implementation of the speed limit policies. This finding was surprising as there is a perception among policymakers that there would have been public backlash against these sorts of policy changes. The commonly used hashtags focused largely on road safety and other potential benefits, for example to air pollution. Conclusions Overall, public attitudes towards the policies were positive, thus policymakers should be less anxious about potential public backlash when considering the scale-up of 20mph speed restrictions.

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