Path Planning for First Responders in the Presence of Moving Obstacles With Uncertain Boundaries

Abstract

In this paper, we study the path planning for first responders in the presence of uncertain moving obstacles. To support the path planning, in our research we use hazard simulation to provide the predicted information of moving obstacles. A major problem in using hazard simulation is that the simulation results may involve uncertainty due to model errors or noise in the real measurements. To address this problem, we provide an approach to handle the uncertainty in the information of moving obstacles, and apply it to the case of toxic plumes. Our contribution consists of two parts: 1) a spatial data model that support representation of uncertain obstacles from hazard simulations and their influence on the road network; 2) a modified A* algorithm that can deal with the uncertainty and generate fast and safe routes passing though the obstacles. The experimental results show the routing capability of our approach and its potential for the application to real disasters.

3d Geospatial Indoor Navigation for Disaster Risk Reduction and Response in Urban Environment

“…It is primarily used for visualization purpose and as a virtual reality training tool (Berlo et al, 2005). However, with time it is emerging as an analytical instrument with more realistic views and simulations for different applications such as plume modelling, forest fire simulation or landslides (Wang et al, 2017;Kemeç et al, 2010).…”

Integration of Traffic Information into the Path Planning among Moving Obstacles

“…A spatial data model is adapted from [7] to structure both the real-time and predicted information of traffic conditions in the road network. Based on the work of [25], we present an extended A* algorithm that can work with both the information of the availabilities and the traffic information of roads, and apply it to generate obstacle-avoiding routes in a traffic network. The remainder of this paper is structured as follows.…”

“…To calculate obstacle-avoiding routes in the traffic network, we design and develop a moving obstacle-avoiding A* (MOAAstar) algorithm based on the algorithm presented in [25]. This algorithm is named MOAAstar-TI.…”

“…In Table 2, we present the calculated results, comparing the results calculated using our MOAAstar-TI algorithm and the results computed using the MOAAstar-II algorithm presented in [25]. We assume that all the three vehicles have to move from the blue point to the green point.…”

Obstacle-Aware Indoor Pathfinding Using Point Clouds

“…Also, considering the 2D representation of indoor environments, a formal definition of an indoor routing graph was presented by Reference [32], in which the presence of obstacles inside rooms was manually represented in the indoor network by adding nodes around the obstacles. A data model to support pathfinding for vehicles among moving obstacles in forest fires was implemented in Reference [33]. Static obstacles such as trees and buildings, and dynamic simulated obstacles such as the spread of fire were considered in the pathfinding.…”