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.

Heterogeneity in robot model mix is advantageous in emerging and future robotic applications. For instance, in a fully automated industrial package loading and unloading scenario, a variety uncertain types of packages need to be continuously sorted and loaded onto designated trucks. To handle the variation of tasks, multiple types of robots, or heterogeneous robots, are designed in the system. However, in such a collaborative system consisting of heterogeneous robots, ineffective task assignments often lead to bad collaboration and thus poor efficiency. To improve the robot collaboration, we herein define the collaborative task assignment problem and develop a fuzzy collaborative intelligence based algorithm to optimize the assignment plans. Specifically, the collaboration type, the collaboration matrix, and the assignment matrix are defined and the new algorithm for adaptive fuzzy collaborative task assignment relies on intuitionistic fuzzy set theory. An unsorted package loading and unloading tasks by heterogeneous robots is used as a case example to validate the new algorithm. Experiments indicate with statistical significance that the new approach shortens total completion time by 21%, reduces total energy consumption by 23%, and increases loading accuracy by 31%, compared with the traditional static task assignment method commonly practiced. The developed approach can be applied to different emerging collaborative systems to improve systems’ collaborative intelligence.

In this paper, we propose a Modified Distributed Bees Algorithm (MDBA) for multi-sensor task allocation in a supply chain security scenario. The MDBA assigns sensors to the upcoming tasks using a decentralized, probabilistic approach to maximize information gain while minimizing costs. Tasks are allocated based on sensors' performance, tasks' priorities and the mutual sensor-task distances. Simulation analysis compared different algorithms and indicated improved performance of 15% by using MDBA with respect to the second-best algorithm.

In the global economy, design of digital media often involves teams of individuals from a variety of cultures who must function together. Similarly, products must be designed and marketed taking specific cultural characteristics into account. Much is known about decision processes, culture and cognition, design of products and interfaces for human interaction with machines, and organizational processes, but this knowledge is dispersed across several disciplines and research areas. This article reviews current work in these areas and proposes a research agenda for fostering increased understanding of the ways in which cultural differences influence decision making and action in design and use of digital media.

Assembly planning is an important step in the design-to-fabrication conversion process. In order to maintain the maximum flexibility, it is important to minimally constrain the planning process. It is also important to generate plans that can be successfully completed in the most effective manner using the available assembly cell. In this research the authors define the minimal precedence constraint concept and address two issues: i) generation and representation of minimal precedence constraints for assembly components and ii) determination of the existence of a global execution plan by analyzing the minimal precedence constraint interactions with cell constraints. The minimal precedence constraint concept is also extended to determining constraints for liaisons as presented by DeFazio and Whitney (1987).

The authors present operational analysis models of autonomous assembly robotic stations. Robotized assembly systems are programmable and therefore provide a cost-effective solution for the assembly of small batch sizes. Assembly-tasks completions and quality considerations require task repetition and rework of a certain portion of the assembled items within the station. Concepts from stochastic processes are used to investigate the structural properties governing the probabilistic relationships of the total and functional batch times and the number of reworks. Further, a model of industrial controlled rework is developed for stations with a bounded number network attempts and distinct rework rates at each trial. Explicit performance and cost measures of the robot operations at the task, product, and assembly-system levels are derived. Capacity design examples illustrate various manufacturing planning considerations such as production throughput, operational efficiency, robot speed, rework rates, and station size.>