Situation awareness

About: Situation awareness is a research topic. Over the lifetime, 7380 publications have been published within this topic receiving 108695 citations. The topic is also known as: SA & situational awareness.

A knowledge processing middleware framework and its relation to the JDL data fusion model

Abstract: Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose successful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a knowledge processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the revised JDL data fusion model.

intehweb.com Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design

Abstract: NASA's vision for space exploration stresses the cultivation of human-robotic systems. Similar systems are also envisaged for a variety of hazardous earthbound applications such as urban search and rescue. Recent research has pointed out that to reduce human workload, costs, fatigue driven error and risk, intelligent robotic systems will need to be a significant part of mission design. However, little attention has been paid to joint human-robot teams. Making human-robot collaboration natural and efficient is crucial. In particular, grounding, situational awareness, a common frame of reference and spatial referencing are vital in effective communication and collaboration. Augmented Reality (AR), the overlaying of computer graphics onto the real worldview, can provide the necessary means for a human-robotic system to fulfill these requirements for effective collaboration. This article reviews the field of human-robot interaction and augmented reality, investigates the potential avenues for creating natural human-robot collaboration through spatial dialogue utilizing AR and proposes a holistic architectural design for human-robot collaboration.

Autonomous river navigation

Abstract: Existing maritime navigation and reconnaissance systems require man-in-the-loop situation awareness for obstacle avoidance, area survey analysis, threat assessment, and mission re-planning. We have developed a boat with fully autonomous navigation, surveillance, and reactive behaviors. Autonomous water navigation is achieved with no prior maps or other data − the water surface, riverbanks obstacles, movers and salient objects are discovered and mapped in real-time using a circular array of cameras along with a self-directed pan-tilt camera. The autonomous boat has been tested on harbor and river domains. Results of the detection, tracking, mapping and navigation will be presented.

Radiolocation and tracking of automatic identification system signals for maritime situational awareness

Abstract: The automatic identification system (AIS), a ship reporting system originally designed for collision avoidance, is becoming a cornerstone of maritime situational awareness. The recent increase of terrestrial networks and satellite constellations of receivers is providing global tracking data that enable a wide spectrum of applications beyond collision avoidance. Nevertheless, AIS suffers the lack of security measures that makes it prone to receiving positions that are unintentionally incorrect, jammed or deliberately falsified. In this study, the authors’ analyse a solution to the problem of AIS data verification that can be implemented within a generic networks of ground AIS base stations with no need for additional sensors or technologies. The proposed approach combines a classic radio-localisation method based on time difference of arrival with an extended Kalman filter designed to track vessels in geodetic coordinates. The approach is validated using anonymised real AIS data collected by multiple base stations that partly share coverage areas. The results show a deviation between the estimated origin of detected signals and the broadcast position data in the order of hundreds of metres, therefore demonstrating the operational potential of the methodology.