The onset of Industry 4.0 has provided considerable benefits to Intelligent Cyber-Physical Systems (ICPS), with technologies such as internet of things, wireless sensing, cognitive computing and artificial intelligence to improve automation and control. However, with increasing automation, the “human” element in industrial systems is often overlooked for the sake of standardization. While automation aims to redirect the workload of human to standardized and programmable entities, humans possess qualities such as cognitive awareness, perception and intuition which cannot be automated (or programmatically replicated) but can provide automated systems with much needed robustness and sustainability, especially in unstructured and dynamic environments. Incorporating tangible human skills and knowledge within industrial environments is an essential function of “Human-in-the-loop” (HITL) Systems, a term for systems powerfully augmented by different qualities of human agents. The primary challenge, however, lies in the realistic modelling and application of these qualities; an accurate human model must be developed, integrated and tested within different cyber-physical workflows to 1) validate the assumed advantages, investments and 2) ensure optimized collaboration between entities. Agricultural Robotic Systems (ARS) are an example of such cyber-physical systems (CPS) which, in order to reduce reliance on traditional human-intensive approaches, leverage sensor networks, autonomous robotics and vision systems and for the early detection of diseases in greenhouse plants. Complete elimination of humans from such environments can prove sub-optimal given that greenhouses present a host of dynamic conditions and interactions which cannot be explicitly defined or managed automatically. Supported by efficient algorithms for sampling, routing and search, HITL augmentation into ARS can provide improved detection capabilities, system performance and stability, while also reducing the workload of humans as compared to traditional methods. This research thus studies the modelling and integration of humans into the loop of ARS, using simulation techniques and employing intelligent protocols for optimized interactions. Human qualities are modelled in human “classes” within an event-based, discrete time simulation developed in Python. A logic controller based on collaborative intelligence (HUB-CI) efficiently dictates workflow logic, owing to the multi-agent and multi-algorithm nature of the system. Two integration hierarchies are simulated to study different types of integrations of HITL: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks and classification accuracy are measured and compared for different collaboration protocols within each hierarchy, to verify the impact of chosen sampling and search algorithms. The experiments performed show the statistically significant advantages of HUB-CI based protocol over traditional protocols in terms of collaborative task performance and disease detectability, thus justifying added investment due to the inclusion of HITL. The results also discuss the competitive factors between both integrations, laying out the relative advantages and disadvantages and the scope for further research. Improving human modelling and expanding the range of human activities within the loop can help to improve the practicality and accuracy of the simulation in replicating an HITL-ARS. Finally, the research also discusses the development of a user-interface software based on ARS methodologies to test the system in the real-world.<br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13303490 |
| Date | 15 December 2020 |
| Creators | Maitreya Sreeram (9706730) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Human-in-the-loop_of_Cyber_Physical_Agricultural_Robotic_Systems/13303490 |
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