This paper focuses on designing a tool for guiding a group of people out of a public building when they are faced with dangerous situations that require immediate evacuation. Despite architectural attempts to produce safe floor plans and exit door placements, people will still commit to fatal route decisions. Since they have access to global views, we believe supervisory people in the control room can use our simulation tools to determine the best courses of action for people. Accordingly, supervisors can guide people to safety. In this paper, we combine Coulomb's electrical law, graph theory, and convex and centroid concepts to demonstrate a computer-generated evacuation scenario that divides the environment into different safe boundaries around the locations of each exit door in order to guide people through exit doors safely and in the most expedient time frame. Our mechanism continually updates the safe boundaries at each moment based on the latest location of individuals who are present inside the environment. Guiding people toward exit doors depends on the momentary situations in the environment, which in turn rely on the specifications of each exit door. Our mechanism rapidly adapts to changes in the environment in terms of moving agents and changes in the environmental layout that might be caused by explosions or falling walls.
The Imperialist Competition Algorithm has recently shown superior performance in optimizing goals. This algorithm has inspired us to apply it to exit doors. The Imperialist Competition Algorithm is applied to an environment to find the best possible locations for exit doors and also to estimate the minimum required width for each door in order to be able to rapidly evacuate a crowd out of danger in emergency situations. The results of our system are applied to a few prototypical scenarios that have demonstrated that the location of each exit door in an indoor space can play a significant role in the evacuation of a crowd out of an emergency situation. Our results thus far are promising and future work will account for more complex floor layouts.
Abstract-This paper explores a strategy for determining public space safety. Due to varied purposes and locations, each public space has architecture as well as facilities. A generalized analysis of capacities for public spaces is essential. The method we propose is to examine a public space with a given architecture. We used Bayesian Belief Network to determine the level of safety and identify points of weakness in public spaces.
Abstract. We present a solution to prevent collisions among robots that are moving toward their respective goals. A robot may start moving at any time from its station to its goal. For a moving robot, the probability of conflict increases proportionately to the complexity of other robots' respective routes. In terms of lowering possibilities of collision, a proper strategy for controlling robot behaviors before encounters is essential. Prior research presented a negotiation-based solution through a broadcasting method. In our solution, we assume robots are unable or unwilling to negotiate or broadcast data among one another. They should possess a strategy to detect and predict conflict zones, and hence determine strategies to avoid collisions independently.
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