79 research outputs found

    Sampling-Based Exploration Strategies for Mobile Robot Autonomy

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    A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later

    Techno-optimization of CO2 transport networks with constrained pipeline parameters

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    In planning large scale carbon sequestration projects, one of the key parameters affecting project economics is the selection of optimal pipeline transportation networks connecting physical locations of carbon sources to sinks (or injection sites). This network is usually determined based on several limiting factors including existing right-of-way, densely populated regions, topology, etc. Open-source tools such as SimCCS2.0 do an effective job in proposing provably optimal routes for construction of new pipelines but are unable to accommodate existing pipelines in techno-economic optimization. With the newly amended 45Q laws offering 70% more tax credits for carbon sequestration than it did in the 2018 amendment, energy companies are looking more into repurposing gas and liquid transportation lines for CO2 transportation to abandoned oil and gas wells for carbon storage and this has further bolstered the need to have a method to account for existing pipelines in sequestration economics. This project demonstrates a method to account for existing pipelines by 1 introducing zero cost paths into the cost surface to represent pipelines, 2 allowing for tie points into the existing pipeline by use of cost exclusion zones around zero cost paths and then, 3 calculating least cost paths and defining transshipment nodes along pipeline intersections. Doing this allowed for a reformulation of the alternate network paths between sources and sinks, and the network was then solved as Minimum-Cost-Network-Flow-Problem (MCNFP) modeled as a mixed integer programming problem. The solution was developed using Python programming language and demo test cases are shown to illustrate the effectiveness of the solution in assessing cost reduction associated with CO2 transfer from sources tied into locations along existing transport pipelines to sinks. This solution has been packaged into a software name Sequestrix and has been made publicly available on GitHub for researchers and economic analysts to take advantage of for evaluating large scale CCUS projects, and to encourage further development and collaboration

    Simulating The Impact of Emissions Control on Economic Productivity Using Particle Systems and Puff Dispersion Model

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    A simulation platform is developed for quantifying the change in productivity of an economy under passive and active emission control mechanisms. The program uses object-oriented programming to code a collection of objects resembling typical stakeholders in an economy. These objects include firms, markets, transportation hubs, and boids which are distributed over a 2D surface. Firms are connected using a modified Prim’s Minimum spanning tree algorithm, followed by implementation of an all-pair shortest path Floyd Warshall algorithm for navigation purposes. Firms use a non-linear production function for transformation of land, labor, and capital inputs to finished product. A GA-Vehicle Routing Problem with multiple pickups and drop-offs is implemented for efficient delivery of commodities across multiple nodes in the economy. Boids are autonomous agents which perform several functions in the economy including labor, consumption, renting, saving, and investing. Each boid is programmed with several microeconomic functions including intertemporal choice models, Hicksian and Marshallian demand function, and labor-leisure model. The simulation uses a Puff Dispersion model to simulate the advection and diffusion of emissions from point and mobile sources in the economy. A dose-response function is implemented to quantify depreciation of a Boid’s health upon contact with these emissions. The impact of emissions control on productivity and air quality is examined through a series of passive and active emission control scenarios. Passive control examines the impact of various shutdown times on economic productivity and rate of emissions exposure experienced by boids. The active control strategy examines the effects of acceptable levels of emissions exposure on economic productivity. The key findings on 7 different scenarios of passive and active emissions controls indicate that rate of productivity and consumption in an economy declines with increased scrutiny of emissions from point sources. In terms of exposure rates, the point sources may not be the primary source of average exposure rates, however they significantly impact the maximum exposure rate experienced by a boid. Tightening of emissions control also negatively impacts the transportation sector by reducing the asset utilization rate as well as reducing the total volume of goods transported across the economy

    A decision support system for integrated semi-centralised urban wastewater treatment systems

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    The importance of adequate water supply and sanitation infrastructure as cornerstones for the development of civilizations is undeniable. Although a strategy based on centralised infrastructure has proven to be successful in the past, in some circumstances such conventional systems are inappropriate for future needs. A Semi-centralised Urban Wastewater Treatment System (SUWWTS) may be considered a viable sustainable urban water management solution to promote water security. A SUWWTS merges regulations of traditional centralised systems with the concepts of close-loop and resource recovery of decentralised systems. However, research on the design and feasibility of implementing semi-centralised systems is in its infancy. This Thesis is a first attempt to articulate the complexity, to systematize and to automatize the design of a SUWWTS. Here we show a novel method, referred to as framework, for the development of SUWWTS with allowance for the socio-economic and geographic context of any urban area. To demonstrate the proposed framework a Decision Support System (DSS) was developed; its output is a recommended design comprised of several wastewater treatment plants, their respective technology, and their associated sewerage and reclaimed water distribution networks. The results demonstrate the capabilities and the usefulness of the DSS; it applies the design engineers’ subjective preferences, such as regional technological inclinations and implementation strategies. The results from a feasibility study on the city of Rio de Janeiro validated and demonstrated how the DSS can be used to assist decision-makers. This Thesis discusses the framework, the DSS and the demonstration case. Overall, it will hopefully help both other researchers and practitioners by contributing to the discussion on how to promote urban water security, to decrease urban areas’ dependency on ecosystem services whilst delivering better social welfare

    Virtual Structure Based Formation Tracking of Multiple Wheeled Mobile Robots: An Optimization Perspective

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    Today, with the increasing development of science and technology, many systems need to be optimized to find the optimal solution of the system. this kind of problem is also called optimization problem. Especially in the formation problem of multi-wheeled mobile robots, the optimization algorithm can help us to find the optimal solution of the formation problem. In this paper, the formation problem of multi-wheeled mobile robots is studied from the point of view of optimization. In order to reduce the complexity of the formation problem, we first put the robots with the same requirements into a group. Then, by using the virtual structure method, the formation problem is reduced to a virtual WMR trajectory tracking problem with placeholders, which describes the expected position of each WMR formation. By using placeholders, you can get the desired track for each WMR. In addition, in order to avoid the collision between multiple WMR in the group, we add an attraction to the trajectory tracking method. Because MWMR in the same team have different attractions, collisions can be easily avoided. Through simulation analysis, it is proved that the optimization model is reasonable and correct. In the last part, the limitations of this model and corresponding suggestions are given

    Application of graph theory to resource distribution policy-based synthesis of industrial symbiosis networks.

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    Masters Degree. University of KwaZulu-Natal, Durban.Industrial symbiosis (IS) involves the repurposing of waste and by-product streams from one chemical industry as feedstock to another. Given the growing environmental and economic concerns, it has become increasingly difficult for industries not to participate in IS. This has encouraged much research into the field, with IS network design being an important optimisation problem in the research space. However, challenges are associated with the creation of IS networks, with transportation costs and resource distribution being key factors. Furthermore, solution strategies are usually complex and neglect the structural features of the network. A possible solution is the use of graph theory for IS network creation. It was hypothesized that structural features of an IS network can evaluate the effect of distribution policies on IS networks created by graph matching algorithms. The Simplex method (SM), Edmonds-Karp algorithm (FF), and the Hungarian method (HM) were adapted to model IS networks, with the intention to establish a ranking in the suitability in creating IS networks. The adaption rendered the algorithms applicable to feasible IS network discovery under different distribution policies. This graph-based approach allowed for the seamless extraction of the network features as graph metrics. Rigorous testing of the adapted algorithms’ performance using graph metrics was done by simulating numerous IS scenarios. It was found that HM identified connections that, on average, minimised transportation costs to the greatest extent. The HM created networks with the smallest travelling distance than those of SM and FF, showing a 9 % and 6.06 % lower value than SM and FF, respectively. Furthermore, HM-IS networks created more stable and fair networks, which was inferred from the graph metrics. To confirm the HM’s apparent superiority in IS network creation, a case study was simulated with the defined distribution policies being modelled from the matching features. Each distribution policy was quantified as a cost from which it was found that HM-IS networks had a 72.5 % and 74.9 % lower overall distribution cost than FF-IS networks and SM-IS networks, respectively. It was concluded that HM is the most suited for IS network creation and that graph-based modelling of IS is a feasible approach.Spelling error in title in original

    Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

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    Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

    Mobile Ad-Hoc Networks

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    Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

    Advances in Reinforcement Learning

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    Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

    Advances in Robot Navigation

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    Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics
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