A Hierarchical Human-Robot Interaction-Planning Framework for Task Allocation in Collaborative Industrial Assembly Processes

In this paper we propose a framework for task allocation in human-robot collaborative assembly planning. Our framework distinguishes between two main layers of abstraction and allocation. In the higher layer we use an abstract world model, incorporating a multi-agent human-robot team approach in order to describe the collaborative assembly planning problem. From this, nominal coordinated skill sequences for every agent are produced. In order to be able to treat humans and robots as agents of the same form, we move relevant differences/peculiarities into distinct cost functions. The layer beneath handles the concrete skill execution. On atomic level, skills are composed of complex hierarchical and concurrent hybrid state machines, which in turn coordinate the real-time behavior of the robot. Their careful design allows to cope with unpredictable events also on decisional level without having to explicitly plan for them, instead one may rely also on manually designed skills. Such events are likely to happen in dynamic and potentially partially known environments, which is especially true in case of human presence. © 2017 IEEEEU/H2020/688857/E

Example of a problem-to-course life cycle in layout and process planning at the MTA SZTAKI learning factories

MTA SZTAKI maintains two learning, demonstration and research facilities which are both set up to host individual student projects and repeated courses likewise. While the physical setting and facility functionalities do already establish a fundamental context linking both types of activities, recent experience has shown additional potential in building up repeatable courses on the outcome of one-off projects. The paper gives an in-progress overview of a project-to-course development based on equipment designed and built around assembly problems of existing industrial products. It is expected that such interlinking of stand-alone projects and repeated courses will become a recurring part of the operation of the facilities, therefore, a roadmap of a project-to-course life cycle is proposed as a working assumption, pending gradual refinement through subsequent experience and integration of other methodologies

Joint Goal Human Robot collaboration-From Remembering to Inferring

The ability to infer goals, consequences of one’s own and others’ actions is a critical desirable feature for robots to truly become our companions-thereby opening up applications in several domains. This article proposes the viewpoint that the ability to remember our own past experiences based on present context enables us to infer future consequences of both our actions/goals and observed actions/goals of the other (by analogy). In this context, a biomimetic episodic memory architecture to encode diverse learning experiences of iCub humanoid is presented. The critical feature is that partial cues from the present environment like objects perceived or observed actions of a human triggers a recall of context relevant past experiences thereby enabling the robot to infer rewarding future states and engage in cooperative goal-oriented behaviors. An assembly task jointly done by human and the iCub humanoid is used to illustrate the framework. Link between the proposed framework and emerging results from neurosciences related to shared cortical basis for ‘remembering, imagining and perspective taking’ is discussed

Flexible human-robot cooperation models for assisted shop-floor tasks

The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier

On Autonomous Robotic Cooperation Capabilities within Factory and Logistic Scenarios

The paper presents the development of a unified functional, algorithmic and Software (Sw) architecture, which can be adopted as a standard for controlling, at action level only, any robotic structure within a given wide class of them; even of reconfigurable type within the class. Such control architecture is therefore deemed very suitable for operating within factory and/or logistic, possibly reconfigurable, scenarios. Moreover, for the few cases of cooperative activities to be established between agents not allowed to be cable connected, an effective coordination policy, based on the exchange of a reduced information set, only regarding the cooperation goals, is developed; and relevant simulative and experimental trials are briefly outlined. Moreover, the advantage of having, in whatever operative condition, the possibility of commanding the involved structures only in terms of the ultimate goals of each action, also seems to be the right basis for having non-negligible improvements within their integration with automated action planning, and even learning, techniques

Assessing worker performance using dynamic cost functions in human robot collaborative tasks

The aim of this research is to develop a framework to allow efficient Human Robot, HR, collaboration on manufacturing assembly tasks based on cost functions that quantify capabilities and performance of each element in a system and enable their efficient evaluation. A proposed cost function format is developed along with initial development of two example cost function variables, completion time and fatigue, obtained as each worker is completing assembly tasks. The cost function format and example variables were tested with two example tasks utilizing an ABB YuMi Robot in addition to a simulated human worker under various levels of fatigue. The total costs produced clearly identified the best worker to complete each task with these costs also clearly indicating when a human worker is fatigued to a greater or lesser degree than expected