Autonomous mobile robotic teams are increasingly used in exploration of indoor environments. Accurate modeling of the world around the robot and describing the interaction of the robot with the world greatly increases the ability of the robot to act autonomously. This paper demonstrates the ability of autonomous robotic teams to find objects of interest. A novel feature of our approach is the object discovery and the use of it to augment the mapping and navigation process. The generated map can then be decomposed into semantic regions while also considering the distance and line of sight to anchor points. The advantage of this approach is that the robot can return a dense map of the region around an object of interest. The robustness of this approach is demonstrated in indoor environments with multiple platforms with the objective of discovering objects of interest.
Mobile robots are already widely used by first responders both in civilian and military operations. Our current goal is to provide the human team with all the information available from an unknown environment quickly and accurate. Also, the robots need to explore autonomous because tele-operating more than two robots is very difficult and demands one person per robot to do it.
In this paper the authors describe the results of several experiments on behalf of the MAST CTA. Our exploration strategies developed for the experiments use from two to nine robots which sharing information are able to explore and map an unknown environment. Each robot has a local map of the environment and transmit the measurements information to a central computer which fusion all the data to make a global map. This computer called map coordinator send exploration goals to the robot teams in order to explore the environment in the fastest way available. The performance of our exploration strategies were evaluated in different scenarios and tested in two different mobile robot platforms.
Tactical situational awareness in unstructured and mixed indoor / outdoor scenarios is needed for urban combat as well as rescue operations. Two of the key functionalities needed by robot systems to function in an unknown environment are the ability to build a map of the environment and to determine its position within that map. In this paper, we present a strategy to build dense maps and to automatically close loops from 3D point clouds; this has been integrated into a mapping system dubbed OmniMapper. We will present both the underlying system, and experimental results from a variety of environments such as office buildings, at military training facilities and in large scale mixed indoor and outdoor environments.