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Adaptive collaborative control
Adaptive collaborative control is the decision-making approach used in hybrid models consisting of finite-state machines with functional models as subcomponents to simulate behavior of systems formed through the partnerships of multiple agents for the execution of tasks and the development of work products. The term “collaborative control” originated from work developed in the late 90’s and early 2000 by Fong, Thorpe, and Baur (1999). It is important to note that according to Fong et al. in order for robots to function in collaborative control, they must be self-reliant, aware, and adaptive.〔 In literature, the adjective “adaptive” is not always shown but is noted in the official sense as it is an important element of collaborative control. The adaptation of traditional applications of control theory in teleoperations sought initially to reduce the sovereignty of “humans as controllers/robots as tools” and had humans and robots working as peers, collaborating to perform tasks and to achieve common goals. Early implementations of adaptive collaborative control centered on vehicle teleoperation.〔〔 Recent uses of adaptive collaborative control cover training, analysis, and engineering applications in teleoperations between humans and multiple robots, multiple robots collaborating among themselves, unmanned vehicle control, and fault tolerant controller design.
Like traditional control methodologies, adaptive collaborative control takes inputs into the system and regulates the output based on a predefined set of rules. The difference is that those rules or constraints only apply to the higher-level strategy (goals and tasks) set by humans. Lower tactical level decisions are more adaptive, flexible, and accommodating to varying levels of autonomy, interaction and agent (human and/or robotic) capabilities.〔 Models under this methodology may query sources in the event there is some uncertainty in a task that affects the overarching strategy. That interaction will produce an alternative course of action if it provides more certainty in support of the overarching strategy. If not or there is no response, the model will continue performing as originally anticipated.
Several important considerations are necessary for the implementation of adaptive collaborative control for simulation. As discussed earlier, data is provided from multiple collaborators to perform necessary tasks. This basic function requires data fusion on behalf of the model and potentially a need to set a prioritization scheme for handling continuous streaming of recommendations. The degree of autonomy of the robot in the case of human–robot interaction and weighting of decisional authority in robot-robot interaction are important for the control architecture. The design of interfaces is an important human system integration consideration that must be addressed. Due to the inherent varied interpretational scheme in humans, it becomes an important design factor to ensure the robot(s) are correctly conveying its message when interacting with humans.
== History ==
The history of adaptive collaborative control began in 1999 through the efforts of Terrence Fong and Charles Thorpe of Carnegie Mellon University and Charles Baur of École Polytechnique Fédérale de Lausanne.〔 Fong et al. believed existing telerobotic practices, which centered on a human point of view, while sufficient for some domains were sub-optimal for operating multiple vehicles or controlling planetary rovers.〔 The new approach devised by Fong et al. focused on a robot-centric teleoperation model that treated the human as a peer and made requests to them in the manner a person would seek advice from experts. In the nominal work, Fong et al. implemented collaborative control design using a PioneerAT mobile robot and a UNIX workstation with wireless communications and distributed message-based computing.〔 Two years later, Fong utilized collaborative control for several more applications, including the collaboration of a single human operator with multiple mobile robots for surveillance and reconnaissance.〔 Around this same time, Goldberg and Chen presented an adaptive collaborative control system that possessed malfunctioning sources.〔 The control design proved to create a model that maintained a robust performance when subjected to a sizeable fraction of malfunctioning sources.〔 In the work, Goldberg and Chen expanded on the definition of collaborative control to include multiple sensors and multiple control processes in addition to human operators as sources. A collaborative, cognitive workspace in the form of a three-dimensional representation developed by Idaho National Laboratory to support understanding of tasks and environments for human operators expounds on Fong’s seminal work which used textual dialogue as the human-robot interaction. The success of the 3-D display provided evidence of the use of mental models for increased team success. During that same time, Fong et al. developed a three dimensional display that was formed via a fusion of sensor data. A recent adaptation of adaptive collaborative control in 2010 was used to design a fault tolerant control system using a Lyapunov function based analysis.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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