Intelligent Exploration and Autonomous Navigation in Confined Spaces

Abstract

Autonomous navigation and exploration in confined spaces are currently setting new challenges for robots. The presence of narrow passages, flammable atmosphere, dust, smoke, and other hazards makes the mapping and navigation tasks extremely difficult. To tackle these challenges, robots need to make intelligent decisions, maximising information while maintaining the safety of the system and their surroundings. In this paper, we present a suite of reasoning mechanisms along with a software architecture for exploration tasks that can be used to underpin the behavior of a broad range of robots operating in confined spaces. We present an autonomous navigation module that allows the robot to safely traverse known areas of the environment and extract features of the unknown frontier regions. An exploration component, by reasoning about these frontiers, provides the robot with the ability to venture into new spaces. From low-level sensory input and contextual information, the robot incrementally builds a semantic network that represents known and unknown parts of the environment and then uses a logic-based, high-level reasoner to interrogate such a network and decide the best course of actions. We evaluate our approach against several mine-like challenging scenarios with different characteristics using a small drone. The experimental results indicate that our method allows the robot to make informed decisions on how to best explore the environment while preserving safety.