Browsing by Subject "Task allocation"

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Open Access
Autonomous multiple teams establishment for mobile sensor networks by SVMs within a potential field
(2012) Nazlibilek, S.
In this work, a new method and algorithm for autonomous teams establishment with mobile sensor network units by SVMs based on task allocations within a potential field is proposed. The sensor network deployed into the environment using the algorithm is composed of robot units with sensing capability of magnetic anomaly of the earth. A new algorithm is developed for task assignment. It is based on the optimization of weights between robots and tasks. The weights are composed of skill ratings of the robots and priorities of the tasks. Multiple teams of mobile units are established in a local area based on these mission vectors. A mission vector is the genetic and gained background information of the mobile units. The genetic background is the inherent structure of their knowledge base in a vector form but it can be dynamically updated with the information gained later on by experience. The mission is performed in a magnetic anomaly environment. The initial values of the mission vectors are loaded by the task assignment algorithm. The mission vectors are updated at the beginning of each sampling period of the motion. Then the teams of robots are created by the support vector machines. A linear optimal hyperplane is calculated by the use of SVM algorithm during training period. Then the robots are classified as teams by use of SVM mechanism embedded in the robots. The support vector machines are implemented in the robots by ordinary op-amps and basic logical gates. Team establishment is tested by simulations and a practical test-bed. Both simulations and the actual operation of the system prove that the system functions satisfactorily. © 2012 Elsevier Ltd. All rights reserved.
Open Access
Safe shared control between pilots and autopilots in the face of anomalies
(Wiley, 2023-06-09) Eraslan, Emre; Yıldız, Yıldıray; Annaswamy, Anuradha M.
As societal drivers of sustainability, efficiency and quality of life become more urgent and intensive, analysis and synthesis of safety critical systems in the face of anomalies become extremely important. In applications such as fully autonomous ground or air transportation, in electrical grids, and healthcare, often there are two decision-makers, one of which is automation, and the other is a human expert. While there have been several studies undertaken to understand the role of automation, and that of human experts, how the two decision-makers can carry out a shared control in a safety-critical system in the face of anomalies has not been investigated in depth. Our focus in this chapter is on two different shared control architectures for a cyber–physical–human system (CPHS), where the decision-making of the human expert is judiciously combined with that of an advanced automation with cyber components of sensing, computation, communication, and actuation. These architectures are evaluated in the context of flight control problems when severe anomalies are present. It is argued that for a successful synthesis of CPHS a granularity assignment of task allocation and timeline has to be carried out which enables the coordination between human and automation, the specific tasks that they carry out, and the timeline associated with these tasks, all in the context of an anomaly. Models of the physical system, the automation, and the pilot using two different shared control architectures are discussed. Validation of these architectures with a simulation study with human-in-the-loop of flight control problems is reported, demonstrating the design of successful CPHS in the presence of severe anomalies.