&lt;title&gt;4D/RCS: a reference model architecture for intelligent unmanned ground vehicles&lt;/title&gt;

Albus, James S.

doi:10.1117/12.474462

Cited by 94 publications

(96 citation statements)

References 8 publications

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“…The process hierarchy in RCS enables the decomposition of subtasks to different agents as well as different planning intervals within a single agent. Each level contains computational elements for sensory processing, world modeling, value judgment, and behavior generation [1]. Situation awareness could be assigned to the higher-level RCS world modeling components with a tight connection to behavior generation and sensory processing.…”

Section: Autonomous Agents and Situation Awarenessmentioning

confidence: 99%

Evaluating Situation Awareness of Autonomous Systems

Gehrke

2009

Performance Evaluation and Benchmarking of Intelligent Systems

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Autonomous systems proved to be very successful in specialized problem domains. But their perception, reasoning, planning and behavior capabilities are generally designed to fit special purposes. For instance, a robotic agent perceives its environment in a way that was defined in advance by a human designer. The agent does not exhibit a certain perception behavior because it actually thinks it would be reasonable to do so. But with an increasing level of autonomy as well as a larger temporal and spatial scope of agent operation higher-level situation analysis and assessment become essential. This paper examines criteria for evaluating situation-awareness of autonomous systems and proposes methods to satisfy them. An example application scenario is presented that provides initial results for evaluating situation-aware systems.

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Section: Autonomous Agents and Situation Awarenessmentioning

confidence: 99%

Evaluating Situation Awareness of Autonomous Systems

Gehrke

2009

Performance Evaluation and Benchmarking of Intelligent Systems

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“…However, they are mostly rather problem-specific and do not follow a general purpose approach, such as for example the perception and control techniques used in autonomous land vehicles [6], [21]. The architectures proposed for this field of application heavily rely on computer vision and make strong assumptions on the topology of the spatial relationships to be monitored [5], [1]. They were not designed with flexibility and maintainability in mind.…”

Section: Related Workmentioning

confidence: 99%

Management of Tracking for Mixed and Augmented Reality Systems

Keitler

Pustka

Huber

et al. 2009

The Engineering of Mixed Reality Systems

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Position and orientation tracking is a major challenge for Mixed / Augmented Reality applications, especially in heterogeneous and wide-area sensor setups. In this article, we describe trackman, a planning and analysis tool which supports the AR-engineer in setup and maintenance of the tracking infrastructure. A new graphical modeling approach based on spatial relationship graphs (SRGs) eases the specification of known as well as the deduction of new relationships between entities in the scene. Modeling is based on reusable patterns representing the underlying sensor drivers or algorithms. Recurring constellations in the scene can be condensed into reusable meta-patterns. The process is further simplified by semiautomatic modeling techniques which automize trivial steps. Dataflow networks can be generated automatically from the SRG and are guaranteed to be semantically correct. Furthermore, generic tools are described that allow for the calibration/registration of static spatial transformations as well as for the live surveillance of tracking accuracy. In summary, this approach reduces tremendously the amount of expert knowledge needed for the administration of tracking setups.

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“…4D/RCS is a method of designing, integrating and testing intelligent systems software for vehicles that have a certain degree of autonomy [81]. It is an autonomous intelligent control system architecture for vehicles that can be either teleoperated or fully autonomous.…”

Section: D/rcs (Real-time Control Systems)mentioning

confidence: 99%