219 research outputs found

    Ped-Air: A Simulator for Loading, Unloading, and Evacuating Aircraft

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    AbstractWe present Ped-Air, a pedestrian simulation system to model the loading, unloading, and evacuation of commercial aircraft. We address the challenge of simulating passenger movement in constrained spaces (e.g., aisles and rows), along with complex, coordinating behaviors between the passengers. Ped-Air models different categories of passengers and flight crew, capturing their unique behaviors and complex interactions. We exhibit Ped-Airs capabilities by simulating passenger movements on two representative aircraft: a single-aisle Boeing 737, and a double-aisle Boeing 777. We are able to simulate the following behaviors: stress, luggage placement, flight staff assisting passengers, obstructed exits for evacuation

    Doctor of Philosophy

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    dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

    Doctor of Philosophy

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    dissertationWildfire is a common hazard in the western U.S. that can cause significant loss of life and property. When a fire approaches a community and becomes a threat to the residents, emergency managers need to take into account both fire behavior and the expected response of the threatened population to warnings before they issue protective action recommendations to the residents at risk. In wildfire evacuation practices, incident commanders use prominent geographic features (e.g., rivers, roads, and ridgelines) as trigger points, such that when a fire crosses a feature, the selected protective action recommendation will be issued to the residents at risk. This dissertation examines the dynamics of evacuation timing by coupling wildfire spread modeling, trigger modeling, reverse geocoding, and traffic simulation to model wildfire evacuation as a coupled human-environmental system. This dissertation is composed of three manuscripts. In the first manuscript, wildfire simulation and household-level trigger modeling are coupled to stage evacuation warnings. This work presents a bottom-up approach to constructing evacuation warning zones and is characterized by fine-grain, data-driven spatial modeling. The results in this work will help improve our understanding and representation of the spatiotemporal dynamics in wildfire evacuation timing and warnings. The second manuscript integrates trigger modeling and reverse geocoding to extract and select prominent geographic features along the boundary of a trigger buffer. A case study using a global gazetteer GeoNames demonstrates the potential value of the proposed method in facilitating communications in real-world evacuation practice. This work also sheds light on using reverse geocoding in other environmental modeling applications. The third manuscript explores the spatiotemporal dynamics behind evacuation timing by coupling fire and traffic simulation models. The proposed method sets wildfire evacuation triggers based on the estimated evacuation times using agent-based traffic simulation and could be potentially used in evacuation planning. In summary, this dissertation enriches existing trigger modeling approaches by coupling fire simulation, reverse geocoding, and traffic simulation. A framework for modeling wildfire evacuation as a coupled human-environmental system using triggers is proposed. Moreover, this dissertation also attempts to advocate and promote open science in wildfire evacuation modeling by using open data and software tools in different phases of modeling and simulation

    Geometric Collision Avoidance for Heterogeneous Crowd Simulation

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    Simulation of human crowds can create plausible human trajectories, predict likely flows of pedestrians, and has application in areas such as games, movies, safety planning, and virtual environments. This dissertation presents new crowd simulation methods based on geometric techniques. I will show how geometric optimization techniques can be used to efficiently compute collision-avoidance constraints, and use these constraints to generate human-like trajectories in simulated environments. This process of reacting to the nearby environment is known as local navigation and it forms the basis for many crowd simulation techniques, including those described in this dissertation. Given the importance of local navigation computations, I devote much of this dissertation to the derivation, analysis, and implementation of new local navigation techniques. I discuss how to efficiently exploit parallelization features available on modern processors, and show how an efficient parallel implementation allows simulations of hundreds of thousands of agents in real time on many-core processors and tens of thousands of agents on multi-core CPUs. I analyze the macroscopic flows which arise from these geometric collision avoidance techniques and compare them to flows seen in real human crowds, both qualitatively (in terms of flow patterns) and quantitatively (in terms of flow rates). Building on the basis of these strong local navigation models, I further develop many important extensions to the simulation framework. Firstly, I develop a model for global navigation which allows for more complex scenarios by accounting for long-term planning around large obstacles or emergent congestion. Secondly, I demonstrate methods for using data-driven approaches to improve crowd simulations. These include using real-world data to automatically tune parameters, and using perceptual user study data to introduce behavioral variation. Finally, looking beyond geometric avoidance based crowd simulation methods, I discuss methods for objectively evaluating different crowd simulation strategies using statistical measures. Specifically, I focus on the problem of quantifying how closely a simulation approach matches real-world data. I propose a similarity metric that can be applied to a wide variety of simulation approaches and datasets. Taken together, the methods presented in this dissertation enable simulations of large, complex humans crowds with a level of realism and efficiency not previously possible.Doctor of Philosoph

    Practical Considerations and Applications for Autonomous Robot Swarms

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    In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical details. Specifically, we introduce three new metrics: (1) the distance complexity (reflecting power usage and wear-and-tear of robots), (2) the spatial complexity (reflecting the space needed for the algorithm to work), and (3) local computational complexity (reflecting the computational requirements for each robot in the swarm). We apply these metrics in the study of some well-known and important problems, such as Complete Visibility and Arbitrary Pattern Formation. We also introduce and study a new problem, Doorway Egress, that captures the essence of a swarm’s navigation through restricted spaces. First, we examine the distance and spatial complexity used across a class of Complete Visibility algorithms. Second, we provide algorithms for Complete Visibility on an integer plane, including some that are asymptotically optimal in terms of time, distance complexity, and spatial complexity. Third, we introduce the problem of Doorway Egress and provide algorithms for a variety of robot swarm models with various optimalities. Finally, we provide an optimal algorithm for Arbitrary Pattern Formation on the grid

    Control techniques for mechatronic assisted surgery

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    The treatment response for traumatic head injured patients can be improved by using an autonomous robotic system to perform basic, time-critical emergency neurosurgery, reducing costs and saving lives. In this thesis, a concept for a neurosurgical robotic system is proposed to perform three specific emergency neurosurgical procedures; they are the placement of an intracranial pressure monitor, external ventricular drainage, and the evacuation of chronic subdural haematoma. The control methods for this system are investigated following a curiosity led approach. Individual problems are interpreted in the widest sense and solutions posed that are general in nature. Three main contributions result from this approach: 1) a clinical evidence based review of surgical robotics and a methodology to assist in their evaluation, 2) a new controller for soft-grasping of objects, and 3) new propositions and theorems for chatter suppression sliding mode controllers. These contributions directly assist in the design of the control system of the neurosurgical robot and, more broadly, impact other areas outside the narrow con nes of the target application. A methodology for applied research in surgical robotics is proposed. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers. However, the lack of conformity to the criteria in the top tier, combined with the inability to conclusively prove increased clinical benefit, particularly symptomatic benefit, is shown to be hampering the potential of surgical robotics in gaining wide establishment. A control scheme for soft-grasping objects is presented. Grasping a soft or fragile object requires the use of minimum contact force to prevent damage or deformation. Without precise knowledge of object parameters, real-time feedback control must be used to regulate the contact force and prevent slip. Moreover, the controller must be designed to have good performance characteristics to rapidly modulate the fingertip contact force in response to a slip event. A fuzzy sliding mode controller combined with a disturbance observer is proposed for contact force control and slip prevention. The robustness of the controller is evaluated through both simulation and experiment. The control scheme was found to be effective and robust to parameter uncertainty. When tested on a real system, however, chattering phenomena, well known to sliding mode research, was induced by the unmodelled suboptimal components of the system (filtering, backlash, and time delays). This reduced the controller performance. The problem of chattering and potential solutions are explored. Real systems using sliding mode controllers, such as the control scheme for soft-grasping, have a tendency to chatter at high frequencies. This is caused by the sliding mode controller interacting with un-modelled parasitic dynamics at the actuator-input and sensor-output of the plant. As a result, new chatter-suppression sliding mode controllers have been developed, which introduce new parameters into the system. However, the effect any particular choice of parameters has on system performance is unclear, and this can make tuning the parameters to meet a set of performance criteria di cult. In this thesis, common chatter-suppression sliding mode control strategies are surveyed and simple design and estimation methods are proposed. The estimation methods predict convergence, chattering amplitude, settling time, and maximum output bounds (overshoot) using harmonic linearizations and invariant ellipsoid sets

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum
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