2,153 research outputs found

    RRT*-SMART: a rapid convergence implementation of RRT*

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    Many sampling based algorithms have been introduced recently. Among them Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle free path finding algorithm. Although it ensures probabilistic completeness, it cannot guarantee finding the most optimal path. Rapidly Exploring Random Tree Star (RRT*), a recently proposed extension of RRT, claims to achieve convergence towards the optimal solution thus ensuring asymptotic optimality along with probabilistic completeness. However, it has been proven to take an infinite time to do so and with a slow convergence rate. In this paper an extension of RRT*, called as RRT*-Smart, has been prposed to overcome the limitaions of RRT*. The goal of the proposecd method is to accelerate the rate of convergence, in order to reach an optimum or near optimum solution at a much faster rate, thus reducing the execution time. The novel approach of the proposed algorithm makes use of two new techniques in RRT*–Path Optimization and Intelligent Sampling. Simulation results presented in various obstacle cluttered environments along with statistical and mathematical analysis confirm the efficiency of the proposed RRT*-Smart algorithm

    Capacity Based Evacuation with Dynamic Exit Signs

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    Exit paths in buildings are designed to minimise evacuation time when the building is at full capacity. We present an evacuation support system which does this regardless of the number of evacuees. The core concept is to even-out congestion in the building by diverting evacuees to less-congested paths in order to make maximal usage of all accessible routes throughout the entire evacuation process. The system issues a set of flow-optimal routes using a capacity-constrained routing algorithm which anticipates evolutions in path metrics using the concept of "future capacity reservation". In order to direct evacuees in an intuitive manner whilst implementing the routing algorithm's scheme, we use dynamic exit signs, i.e. whose pointing direction can be controlled. To make this system practical and minimise reliance on sensors during the evacuation, we use an evacuee mobility model and make several assumptions on the characteristics of the evacuee flow. We validate this concept using simulations, and show how the underpinning assumptions may limit the system's performance, especially in low-headcount evacuations

    Modelling Driver Interdependent Behaviour in Agent-Based Traffic Simulations for Disaster Management

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    Accurate modelling of driver behaviour in evacuations is vitally important in creating realistic training environments for disaster management. However, few current models have satisfactorily incorporated the variety of factors that affect driver behaviour. In particular, the interdependence of driver behaviours is often seen in real-world evacuations, but is not represented in current state-of-the art traffic simulators. To address this shortcoming, we present an agent-based behaviour model based on the social forces model of crowds. Our model uses utility-based path trees to represent the forces which affect a driver's decisions. We demonstrate, by using a metric of route similarity, that our model is able to reproduce the real-life evacuation behaviour whereby drivers follow the routes taken by others. The model is compared to the two most commonly used route choice algorithms, that of quickest route and real-time re-routing, on three road networks: an artificial "ladder" network, and those of Lousiana, USA and Southampton, UK. When our route choice forces model is used our measure of route similarity increases by 21%-93%. Furthermore, a qualitative comparison demonstrates that the model can reproduce patterns of behaviour observed in the 2005 evacuation of the New Orleans area during Hurricane Katrina
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