The Distributed and Collaborative Intelligent Systems and Technology (DCIST) CRA aims to study the underlying science questions and develop new methods and concepts that will enable these abilities through behaviors that are not brittle and preprogrammed but rather adaptive, resilient, and learned. Thrust 1 – Multi-Robot Collaborative Autonomy handles a large team of autonomous robots acquiring information from an unknown and dynamic environment while each agent performs individual and team complex missions. In this paper, we present an overview of this thrust and experimental results about robust navigation, distributed semantic mapping and localization, multi-agent communication, decision-making and maximization of knowledge about adversarial dynamic targets.
KEYWORDS: Robotics, Communication and information technologies, Information operations, Sensors, Data modeling, 3D modeling, Visual process modeling, Unmanned ground vehicles, Situational awareness sensors
The multi-agent Value of Information (MAVoI) problem is concerned with the capacity of autonomous agents to evaluate the estimated value of available information from and for other agents. Such a paradigm would allow agents to limit information sharing under network constraints, environmental and adversarial, and also to decide when to prioritize inter-agent communications over further information-gathering. In our work, we contribute to the conceptual development of an MAVoI framework by expanding upon a previous paradigm [1] that connected the notion of the Value of Information (VoI), as it originates from decision theory, to the body of work on the Quality of Information (QoI/IQ). Within this paradigm, QoI characterizes the intrinsic attributes of information objects that may be used to judge them, while VoI judges their context- and user-specific utility and fitness-for-use. For teams of multiple, autonomous agents with distributed intelligence, we propose an additional category: the Affinity of Information (AoI). Since classical VoI quantifies the gain in value that comes from more information regarding the expected value of available actions, we propose AoI as those metrics and features that characterize the change in an agent’s state model. We then conceptually illustrate our proposed MAVoI taxonomy in the context of a distributed Simultaneous Localization and Mapping (SLAM) task, in which the mission objective is the localization of multiple objects of interest.
Robots, equipped with powerful modern sensors and perception algorithms, have enormous potential to use what they perceive to provide enhanced situational awareness to their human teammates. One such type of information is changes that the robot detects in the environment that have occurred since a previous observation. A major challenge for sharing this information from the robot to the human is the interface. This includes how to properly aggregate change detection data, present it succinctly for the human to interpret, and allow the human to interact with the detected changes, e.g., to label, discard, or even to task the robot to investigate, for the purposes of enhanced situational awareness and decision making. In this work we address this challenge through the design of an augmented reality interface for aggregating, displaying, and interacting with changes detected by an autonomous robot teammate. We believe the outcomes of this work could have significant applications to Soldiers interacting with any type of high-volume, autonomously-generated information in Multi-Domain Operations.
KEYWORDS: 3D modeling, Robotics, Clouds, 3D acquisition, Mobile robots, Improvised explosive devices, Sensors, Navigation systems, Systems modeling, Data acquisition
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.
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