Human-in-the-loop: Role in Cyber Physical Agricultural Systems

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS

Design of Protocols for Task Administration in Collaborative Production Systems

Customer-focused and concurrent engineering service systems process tasks more effectively as a result of the power of collaboration among multiple participants. In such environments, however, complex situations might arise that require decisions beyond simple coordination.Task Administration Protocols (TAPs) are designed as a control mechanism to manage complex situations in collaborative task environments. This article presents the design of TAPs for collaborative production systems in which tasks are performed by the collaboration of multiple agents. Three component protocols are found to constitute TAPs and are triggered at appropriate stages in task administration: 1) Task Requirement Analysis Protocol, 2) Shared Resource Allocation Protocol, and 3) Synchronization & Time-Out Protocol. A case study with TAPs metrics for task allocation in a collaborative production system is investigated to compare performance under TAPs, and under a non-TAP coordination protocol (which is considered to be simpler). In terms of task allocation ratio, the case study indicates that performance under TAPs is significantly better (up to 10.6%) than under the non-TAP coordination protocol, especially under medium or high load conditions. The advantage of TAPs can be explained by their design with relatively higher level of collaborative intelligence, addressing more complex control logic compared with non-TAP coordination protocols

Reference Architecture for Collaborative Design

Issues and themes of Collaborative Design (CD) addressed by research done so far are so extensive that when running a project of collaborative design, people may lack directions or guidelines to support the whole picture. Hence, developing reference architecture for CD is important and necessary in the academic and the empirical fields. Reference architecture provides the systematic, elementary skeleton and can be extended and adapted to diverse, changing environments. It also provides a comprehensive framework and enables practices implemented more thoroughly and easily. The reference architecture developed in this re-search is formed along three dimensions: decision aspect, design stage, and collaboration scope. There are five elements in the dimension of decision aspect: (1) participant, (2) product, (3) process, (4) organization, and (5) information. The dimension of design stage includes three stages: (1) planning and concepting, (2) system-level design and detail design, and (3) testing and prototyping. The dimension of collaboration scope includes three types of collaboration: (1) cross-functional, (2) cross-company, and (3) cross-industry. Because of the three reference dimensions, a cubic architecture is developed. The cubic reference architecture helps decision-makers in dealing with implementing a CD project or activity. It also serves as a guideline for CD system developers or people involved in the design collaboration to figure out their own responsibility functions and their relations with other members. Demonstration of how to use the reference architecture in developing design collaboration activities and specifying the details for cross-company CD is also provided in this research

Collaborative Control Theory and Decision Support Systems

Collaborative Decision Support Systems, CDSS, depend on cost-effective collaboration among the decision participants. Those may include, in addition to human decision makers, non-human entities such as robots, software and hardware agents, sensors, and autonomous instruments. The purpose of this article is to explore the impact that CCT, the Collaborative Control Theory, has on cyber supported augmentation of collaboration in general, and its proven and potential impacts on CDSS in particular. Three recent case studies are discussed. The correlation between CDSS decision process and quality; and the level of CCT-based collaboration augmentation and the resulting level of Collaborative Intelligence, CI, is presented. It is concluded that while there are clear positive impacts of CCT based augmentation and level of CI, they need to be measured and optimized, not maximized. Further research in this area is also described