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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. 

Mobile robots and IoT (Internet of Things) devices can increase productivity, but only if they can be programmed by workers who understand the domain. This is especially true in manufacturing. Visual programming in the spatial context of the operating environment can enable mental models at a familiar level of abstraction. However, spatial-visual programming is still in its infancy; existing systems lack IoT integration and fundamental constructs, such as functions, that are essential for code reuse, encapsulation, or recursive algorithms. We present Vipo, a spatial-visual programming system for robot-IoT workflows. Vipo was designed with input from managers at six factories using mobile robots. Our user study (n=22) evaluated efficiency, correctness, comprehensibility of spatial-visual programming with functions.