Workflow composition and analysis in Industry 4.0 warehouse automation

Abstract

Workflow compositions have been exploited in business process modelling to handle concurrent invocations of modular components. With the emergence of Industry 4.0 warehouse automation, which enable the integration of business processes, mechanised robots, sensor–actuators and human participants, analysis and specification of workflows become crucial. As such environments have dynamic deployments due to varying demand rates and environmental conditions, the workflow compositions are intended to be adaptable to runtime changes. In addition, monitoring the end‐to‐end latency and optimal runtime binding is critical in industrial deployments such as warehouse automation. The authors provide specifications in the concurrent programming language Orc that supports most commonly used workflow patterns. Complex deployments involving multiple robotic agents and business processes further require analysis of correctness, liveness, and safety properties. In order to verify the workflows, the Orc specifications are translated into workflow net representations, with verification done using the TAPAAL model checker. The advantages of deploying fine grained analysis of workflows are demonstrated over picker/delivery robots involved in warehouse operations. The envisioned set of reusable specifications may be extended and applied to a variety of Industry 4.0 deployments to handle complex workflow interactions.

Cloud‐based production logistics synchronisation service infrastructure for customised production processes

“…The real‐time information perception and integration framework of the IoMT provide technical guidance for solving the problem of real‐time information acquisition of logistics resource awareness [4]. Under this framework, real‐time monitoring of production resources such as machine [22] and warehouses [23, 24] can be realised by deploying a sensing environment consisting of radio‐frequency identification (RFID) tags, card readers, and sensors. After obtaining real‐time information, processing and managing real‐time information can add value to the information [25, 26].…”

A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

“…The scenario we consider is shown in Figure 1. A mobile manipulator (in our case, a Kuka youBot) picks-up objects to be inspected (wooden pieces) from a warehouse area (a marked region in the workspace), and carries them out to deliver them to human operators or another robot (in our case, a dual-arm Baxter manipulator) for inspection [9]. When the object to inspect is delivered to human operators, these undertake the foreman task [10], [11], and then the object is passed to the manipulator for a further vision-based inspection.…”