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.