428 research outputs found

    Solving the Task Variant Allocation Problem in Distributed Robotics

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    We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively

    Ultrasonic sensor platforms for non-destructive evaluation

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    Robotic vehicles are receiving increasing attention for use in Non-Destructive Evaluation (NDE), due to their attractiveness in terms of cost, safety and their accessibility to areas where manual inspection is not practical. A reconfigurable Lamb wave scanner, using autonomous robotic platforms is presented. The scanner is built from a fleet of wireless miniature robotic vehicles, each with a non-contact ultrasonic payload capable of generating the A0 Lamb wave mode in plate specimens. An embedded Kalman filter gives the robots a positional accuracy of 10mm. A computer simulator, to facilitate the design and assessment of the reconfigurable scanner, is also presented. Transducer behaviour has been simulated using a Linear Systems approximation (LS), with wave propagation in the structure modelled using the Local Interaction Simulation Approach (LISA). Integration of the LS and LISA approaches were validated for use in Lamb wave scanning by comparison with both analytical techniques and more computationally intensive commercial finite element/diference codes. Starting with fundamental dispersion data, the work goes on to describe the simulation of wave propagation and the subsequent interaction with artificial defects and plate boundaries. The computer simulator was used to evaluate several imaging techniques, including local inspection of the area under the robot and an extended method that emits an ultrasonic wave and listens for echos (B-Scan). These algorithms were implemented in the robotic platform and experimental results are presented. The Synthetic Aperture Focusing Technique (SAFT) was evaluated as a means of improving the fidelity of B-Scan data. It was found that a SAFT is only effective for transducers with reasonably wide beam divergence, necessitating small transducers with a width of approximately 5mm. Finally, an algorithm for robot localisation relative to plate sections was proposed and experimentally validated

    Disruption Management of ASAE's Inspection Routes

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    The Rapid development and the emergence of technologies capable of producing real-time data opened new horizons to both planning and optimization of vehicle routes [4]. In this dissertation, the Autoridade de Segurança Alimentar e Económica (ASAE) operation's scenario will be explored and analyzed as a case study to the problem. ASAE is a Portuguese administrative authority specialized in food security and economic auditing and is responsible to regulate thousands of economic entities in the Portuguese territory. ASAE inspections are usually done by brigades using vehicles to inspect economic operators, taking into account their timetables. Previous work on this topic led to the implementation of an inspection route optimization module capable of defining and assigning routes to inspect economic operators, seeking to maximize a utility function. Using optimization algorithms, inspection routes are calculated for each brigade, with information regarding specific map paths and inspection schedules. The approach used does not take into consideration the dynamic properties of real-life scenarios, as the precalculated operation plan is not reviewed in real-time. This work aims to study the dynamic properties of ASAE's operational environment and proposes a solution to efficiently review the precalculated inspection routes and apply the required changes in an appropriate time frame. Vehicle routing problems (VRP) are optimization problems where the aim is to calculate the set of optimized routes for a vehicle fleet, from a starting point to several interesting locations. Dynamic vehicle routing problem (DVRP) is a variant of VRP that makes use of real-time information to calculate the most optimized set of routes at a certain moment [39]. DVRP is a challenging problem because its scope is real-time, meaning that decisions sometimes must be made in short time windows, preventing the use of complex algorithms that require long computational times [10]. The typical approach to this problem is to initially calculate the routes for the whole fleet and dynamically revise the defined operations plan in real-time, once a disruption occurs. This work will model the problem as a DVRP and will compare the performance of heuristics and other modern optimization techniques, proposing a solution that will reduce the impact of disruptions on inspection routes. An optimized operations plan will reduce the time required for inspections, allowing massive economic savings, while reducing a company's ecological footstep. The work can eventually be scaled and used in other institutions, such as GNR or PSP in Portugal, that operate similarly

    Hybrid approaches for mobile robot navigation

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    The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

    Learning cognitive maps: Finding useful structure in an uncertain world

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    In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

    Development of the Next Generation of Water Distribution Network Modelling Tools Using Inverse Methods

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    The application of optimisation to Water Distribution Network (WDN) Modelling involves the use of computer-based techniques to many different problems, such as leakage detection and localisation. The success in the application of any model-based methodology for finding leaks highly depends on the availability of a well-calibrated model. Both leak detection and localisation, as well as model calibration are procedures that constitute the field of inverse problems in WDN modelling. The procedures are interlinked and dependent as when a leak is found and the model is updated its quality improves, while when a model is calibrated its ability to detect and localise leaks also improves. This is because both inverse problems are solved with the aim to mimic the behaviour of the real system as closely as possible using field measurements. In this research, both inverse problems are formulated as constrained optimisation problems. Evolutionary Optimisation techniques, of which Genetic Algorithms are the best-known examples, are search methods that are increasingly applied in WDN modelling with the aim to improve the quality of a solution for a given problem. This, ultimately, aids practitioners in these facets of management and operation of WDNs. Evolutionary Optimisation employs processes that mimic the biological process of natural selection and “survival of the fittest” in an artificial framework. Based on this philosophy a population of individual solutions to the problem is manipulated and, over time, “evolves” towards optimal solutions. However, such algorithms are characterised by large numbers of function evaluations. This, coupled with the computational complexity associated with the hydraulic simulation of WDNs incurs significant computational burden, can limit the applicability and scalability of this technology across the Water Industry. In addition, the inverse problem is often “ill-posed”. In practice, the ill-posed condition is typically manifested by the non-uniqueness of the problem solution and it is usually a consequence of inadequate quantity and/or quality of field observations. Accordingly, this thesis presents a methodology for applying Genetic Algorithms to solve leakage related inverse problems in WDN Modelling. A number of new procedures are presented for improving the performance of such algorithms when applied to the complex inverse problems of leak detection and localisation, as well as model calibration. A novel reformulation of the inverse problem is developed as part of a decision support framework that minimizes the impact of the inherent computational complexity and dimensionality of these problems. A search space reduction technique is proposed, i.e., a reduction in the number of possible solution combinations to the inverse problem, to improve its condition considering the accuracy of the available measurements. Eventually, this corresponds to a targeted starting point for initiating the search process and therefore more robust stochastic optimisations. The ultimate purpose is to increase the reliability of the WDN hydraulic model in localising leaks in real District Metered Areas, i.e., to reduce the number false positives. In addition, to speed up the leak search process (both computationally and physically) and, improve the overall model accuracy. A calibrated model of the WDN is not always available for supporting work at distribution mains level. Consequently, two separate problem-specific methods are proposed to meet the abovementioned purpose: (a) a Leak Inspection Method used for the detection and localisation of leaks and; (b) a Calibration Method for producing an accurate average day model that is fit for the purpose of leak detection and localisation. Both methods integrate a three-step Search Space Reduction stage, which is implemented before solving the inverse problem. The aim is to minimize the number of decision variables and the range of possible values, while trying to preserve the optimum solution, i.e., reduce the inverse problem dimensionality. The search space reduction technique is established to generate a reduced set of highly sensitive decision variables. Eventually this is done to provide a viable, scalable technique for accelerating evolutionary optimisation applications in inverse problems being worthwhile on both academic and practical grounds. The novel methodologies presented here for leak detection and localisation, as well as for model calibration are verified successfully on four case studies. The case studies include two real WDN examples with artificially generated data, which investigate the limits of each method separately. The other two case studies implement both methods on real District Metered Areas in the United Kingdom, firstly to calibrate the hydraulic network model and, then, to detect and localise a single leak event that has actually happened. The research results suggest that leaks and unknown closed or open throttle valves that cause a hydraulic impact larger than the sensor data error can be detected and localised with the proposed framework which solves the inverse problem after search space reduction. Moreover, the quality of solutions can dramatically improve for given runtime of the algorithm, as 99.99% of infeasible solution combinations are removed, compared to the case where no search space reduction is performed. The outcomes of the real cases show that the presented search space reduction technique can reduce the search area for finding the leak to within 10% of the WDN (by length). The framework can also contribute to more timely detection and localisation of leakage hotspots, thus reducing economic and environmental impacts. The optimisation model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and the physical measurement limitations from the pressure and flow field tests

    On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

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    Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

    Hydrolink 2021/2. Artificial Intelligence

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    Topic: Artificial Intelligenc
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