Robot exploration in unknown cluttered environments when dealing with uncertainty
Farzad Niroui, Ben Sprenger, Goldie Nejat · 2017
The use of autonomous robots in urban search and rescue (USAR) missions has many potential benefits in terms of assisting rescue workers and increasing efficiency in these time- critical environments. However, the cluttered and unknown nature of these environments introduces uncertainty in both the sensing and actuation capabilities of a rescue robot. Such uncertainty has not been directly incorporated into the modeling of the USAR problem for existing robots. In this paper, we present the novel use of a partially observable Markov Decision Process (POMDP) method which directly incorporates uncertainty within the decision-making layer of the controller for a rescue robot. A hierarchical task structure is used to decompose the overall exploration and victim identification task of a robot into smaller subtasks. These subtasks are modeled as POMDPs taking into account sensory and actuation uncertainty. Our proposed approach was tested in numerous experiments in unknown and cluttered USAR-like environments. The results should that the approach was able to successfully explore the environments and find victims, while dealing with sensor and actuator uncertainty.