Multi-agent Simulation Scenarios for Evacuation within Children's Facilities through Merged Machine Learning Techniques and Multilayer Vulnerability Analysis

Abstract

Evacuation plans in buildings where people perform activities must be clearly defined. Children's facilities are a special case in which indoor navigation must be traced by safe routes. However, usually, the routes follow the shortest path. We propose the calculation of safer evacuation routes inside a multi-agent kindergarten environment using the angle propagation theta*-multilayer vulnerability analysis (AP-Theta*-MVA) algorithm, a novel variant of the angle propagation theta* (AP-Theta*) pathfinding technique. In this variant, we perform the multilayer vulnerability analysis (MVA) of geometric objects based on international standards to obtain importance indexes (Sn). In addition, we include rules of the reciprocal n-body collision avoidance approach (ORCA) and the conditioning variables of the location of the hazard, the number of people, and their speed of movement and reaction ability. We apply the algorithm in different scenarios of evacuation due to fire smoke propagation within a children's facility. Our results show that for each scenario, AP-Theta*-MVA provides orders through signals obtained by supervised learning to the multi-agent system to react and move away from dangerous areas. Thus, we achieve safer evacuation patterns and routes for a multi-agent system. This demonstrates the suitability of the AP-Theta*-MVA algorithm, which is influenced by the MVA, for children's facilities when it is performed in a multi-agent system, enabling the calculation of safe and feasible evacuation routes with realistic times to improve evacuation plans.