Multi-agent robotic system (MARS) for UAV-UGV path planning and automatic sensory data collection in cluttered environments

Abstract

There has been growing interest in increasing the application of robotic and automation technologies for building indoor inspection. However, much previous research on indoor robotic applications was limited to a single type of unmanned aerial/ground vehicle (UAV/UGV), each of which has certain limitations and constraints. Besides, the robotic systems suffer from inefficient control within cluttered indoor environments containing many obstacles. This paper presents a multi-agent robotic system (MARS) for automatic UAV-UGV path planning and indoor navigation to automate sensory data collection. The proposed MARS consists of a new system architecture that defines the attributes and data requirements for UAV and UGV indoor path planning. To improve indoor navigation in cluttered environments, an enhanced shunting short-term memory model is established to optimize the pathfinding of UAV/UGV for data collection. Assessment of indoor navigation is conducted with a simulation-based approach and LiDAR SLAM. A mediating agent, which harnesses a control algorithm and information exchange mechanism, is proposed to interoperate UAV and UGV for automated data collection. The proposed new MARS is examined in experiments, in which a single UAV, dual UAVs, and combined UAV-UGV are tested in a research laboratory. The result indicates that the MARS can support automated path planning and indoor navigation for 2D image and 3D point cloud data collection.