Distributed Control of Heterogeneous Robotic System for Area Exploration

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The main objective of this research is to develop a distributed control system for autonomous operations of multiple heterogeneous robots to collaboratively perform a task/tasks in an uncertain environment. The heterogeneous robots include both ground mobile robots and micro-aerial vehicles (MAV) having different sensor and control capabilities. The capabilities developed in this research can be extended for a variety of industrial applications, such as surveillance, exploration and mapping of areas that are not accessible for humans (e.g. abandoned mines or hazardous areas), underground mining, search and rescue, and also for other tasks involving multi-robotic coordination. The research specifically develops methods to address distributed localization, exploration and control architecture of combined ground aerial teams. MAV`s are progressively becoming the preferred choice for indoor surveillance and search operations. The high degrees of freedom of the MAV allows navigation in cluttered indoor settings. It can maneuver through multi floor buildings with fast searches in a 3D perspective, relative to its ground based alternatives. The limited payload of MAV`s introduce constraints related to flight time, sensors and processing power of the devices, crippling its localization ability and effectiveness of the overall mission. On the other hand the ground robots are available with a range of sizes and they have the ability to provide faster processing, long range sensing and highly accurate localization information. Therefore, exploration missions are progressively moving towards multi agent implementations to harness the relative capabilities. The proposal includes several main research components; a higher level control architecture for distributed control of aerial and ground robots, formation control and tracking using visual attention, shared and mutual localization of different vehicles, MAV pose estimate and trajectory control, and finally sensor fusion and data analysis for localization and mapping (SLAM).