An overview of Nasrem - The NASA/NBS standard reference model for telerobot control system architecture

Albus, James S.; Quintero, Richard; Lumia, Ronald

doi:10.2514/6.1994-4608

Cited by 60 publications

(69 citation statements)

References 5 publications

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“…From early work [11] there has been general convergence on a three level approach. However, to achieve the additional reactive deliberation that certification requires, we here revert to a four level model (fig 1).…”

Section: Architecture Overviewmentioning

confidence: 99%

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Design and evaluation of a reactive and deliberative collision avoidance and escape architecture for autonomous robots

et al. 2007

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We present the design and evaluation of an architecture for collision avoidance and escape of mobile autonomous robots operating in unstructured environments. The approach mixes both reactive and deliberative components. This provides the vehicle's behaviour designers with an explicit means to design-in avoidance strategies that match system requirements in concepts of operations and for robot certification. The now traditional three layer architecture is extended to include a fourth Scenario layer, where scripts describing specific responses are selected and parameterised on the fly. A local map is maintained using available sensor data, and adjacent objects are combined as they are observed. This has been observed to create safer trajectories. Objects have persistence and fade if not re-observed over time. In common with behaviour based approaches, a reactive layer is maintained containing pre-defined knee jerk responses for extreme situations. The reactive layer can inhibit outputs from above. Path planning of updated goal point outputs from the Scenario layer is performed using a fast marching method made more efficient through lifelong planning techniques. The architecture is applied to applications with Autonomous Underwater Vehicles. Both simulated and open water tests are carried out to establish the performance and usefulness of the approach.

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Section: Architecture Overviewmentioning

confidence: 99%

“…Within the classic functional architecture [11], the obstacle avoidance function is devolved to very low level modules and works in isolation. Behavioural approaches [33][12] [13] are the exception.…”

Section: B Prior Artmentioning

confidence: 99%

Design and evaluation of a reactive and deliberative collision avoidance and escape architecture for autonomous robots

et al. 2007

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“…One method is to incorporate humans as a system module [8]. Another method is prioritized control, the classic example of which is NASREM [1]. In both these approaches, the human only provides command input.…”

Section: Related Researchmentioning

confidence: 99%

Robot as Partner: Vehicle Teleoperation with Collaborative Control

Fong

Thorpe

Baur

2002

Multi-Robot Systems: From Swarms to Intelligent Automata

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We have developed a new teleoperation system model called collaborative control. With this model, the robot asks the human questions, to obtain assistance with cognition and perception during task execution. This enables the human to support the robot and to compensate for inadequacies in autonomy. In the following, we review the system models conventionally used in teleoperation, describe collaborative control, and discuss its use.

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“…' Several authors have proposed different architectures for mission adaptive systems. Albus et al [8] propose a model of an "intelligent machine system" that lays out a system with situational assessment feeding information on world models and value judgments that lead to behaviour generation. These are key concepts that are recurrent in many of the architectures and decision systems that have been proposed since.…”

Section: Review Of Mission Adaptive Systemsmentioning

confidence: 99%

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

Brito

Bose

Lewis

et al. 2012

2012 IEEE/OES Autonomous Underwater Vehicles (AUV)

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Abstract-The Autonomous Underwater Vehicle (AUV) community has for many years recognized the potential benefits made by adapting mission planning on-the-fly. Over the years there has been some degree of success in applying adaptive mission planning to very specific problems. Examples of applications include capabilities for a vehicle to search for, and then modify its trajectory to follow, a feature such as a plume or a thermocline, or to modify its trajectory to avoid an obstacle, or to find and follow a feature such as a pipeline. Despite an evident increase in the number of applications, the use of adaptive mission planning is still in its infancy. There is no doubt that adaptive mission planning will play a pivotal role in future AUV persistent presence. So what is delaying this technology from making the leap towards wider industry acceptance? This paper reviews the literature in adaptive mission planning and uses a failure analysis technique to identify key obstacles for the integration of this technique in wider AUV applications. We use our failure analysis to help devise recommendations for mitigating these obstacles. The complexity of the mathematical approaches used by adaptive techniques is one key obstacle. Perhaps of more importance is that the AUV community is increasingly requiring quantitative assessment of risk associated with the use of AUVs. We propose that probability is the appropriate measure for quantifying the risk of adaptive systems and their uncertainty. The work here presented is a collective endeavor of the Engineering Committee on Oceanic Resources Specialist Panel on Underwater Vehicles.

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An overview of Nasrem - The NASA/NBS standard reference model for telerobot control system architecture

Cited by 60 publications

References 5 publications

Design and evaluation of a reactive and deliberative collision avoidance and escape architecture for autonomous robots

Design and evaluation of a reactive and deliberative collision avoidance and escape architecture for autonomous robots

Robot as Partner: Vehicle Teleoperation with Collaborative Control

The Role of adaptive mission planning and control in persistent autonomous underwater vehicles presence

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