Optimal Path Planning for a Remote Sensing Unmanned Ground Vehicle in a Hazardous Indoor Environment

Abstract

Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments.

Path Planning for Autonomous Mobile Robots: A Review

“…The improvement proposed by Park et al [ 197 ] uses a hierarchical structure that reduces the number of samples. PRM has been tested in robots within simulated indoor scenarios in the work of Alenezi et al [ 1 ]. Ichter et al [ 198 ] presented Critical PRM, an algorithm that combines PRM with Reinforcement Learning to determine critical locations such as narrow corridors.…”

“…For instance, this path may be the one entailing the least amount of time. This is critical in missions such as those of the search-and-rescue field [ 1 ]: victims of a disaster may ask for help in life-or-death situations. Another optimization function to consider could be the energy of the robot.…”