Enhancing learning capabilities of movement primitives under distributed probabilistic framework for flexible assembly tasks

This paper presents a novel probabilistic distributed framework based on movement primitives for flexible robot assembly. Since the modern advanced industrial cell usually deals with various scenarios that are not fixed via-point trajectories but highly reconfigurable tasks, the industrial robots used in these applications must be capable of adapting and learning new in-demand skills without programming experts. Therefore, we propose a probabilistic framework that could accommodate various learning abilities trained with different movement-primitive datasets, separately. Derived from the Bayesian Committee Machine, this framework could infer new adapting trajectories with weighted contributions of each training dataset. To verify the feasibility of our proposed imitation learning framework, the simulation comparison with the state-of-the-art movement learning framework task-parametrised GMM is conducted. Several key aspects, such as generalisation capability, learning accuracy and computation expense, are discussed and compared. Moreover, two real-world experiments, i.e. riveting picking and nutplate picking, are further tested with the YuMi collaborative robot to verify the application feasibility in industrial assembly manufacturing.

Human-robot collision predictor for flexible assembly

<p class="Abstract"><span lang="EN-US">The performance of human–robot collaboration can be improved in some assembly tasks when a robot emulates the effective coordination behaviours observed in human teams. However, this close collaboration could cause collisions, resulting in delays in the initial scheduling. Besides the commonly used acoustic or visual signals, vibrations from a mobile device can be used to communicate the intention of a collaborative robot (cobot). In this paper, the communication time of a virtual reality and depth camera-based system is presented in which vibration signals are used to alert the user of a probable collision with a UR5 cobot. Preliminary tests are carried out on human reaction time and network communication time measurements to achieve an initial picture of the collision predictor system’s performance. Experimental tests are also presented in an assembly task with a three-finger gripper that functions as a flexible assembly device.</span></p>

Cognitive Mechatronic Devices for Reconfigurable Production of Complex Parts

This paper discusses a robotic cell that handles geometrically complex products, exploiting cognitive control and actuation systems for the manipulation, assembly and packaging. The individual mechatronic components, namely a 6-DoF gripper and a flexible assembly mechanism, have been designed via functional decomposition of the actual assembly and handling tasks. The flexibility of these mechanisms is exploited through control modules, performing different cognition functions at cell, resource and device level. The design approach can be generalized for tasks requiring dexterity and adaptation to products. A case study from the consumer goods sector, showcases the system’s reconfigurability and efficiency.

SOFTWARE COMPLEX FOR PARTS RECOGNITION AS THE BASIS OF EDUCATIONAL LABORATORY WORK

The article discusses the scientific and methodological foundations of laboratory work in vision systems using the author's algorithms for pattern recognition. The results were used to prepare masters of technical specialties at the Pskov State University. Another approach to using digital technologies for processing images of the working area is proposed. Some aspects of solving problems of identification of parts, determination of their location, control in automated assembly is described. The hardware-software complex in the article performs data processing and measurements in parallel with the flow of the technological process. The hardware and software complex expands the capabilities of flexible assembly platforms when assembling parts with different mass-inertial characteristics due to the geometric shape and dissimilar materials.

Simulation analysis of a multi-product flexible assembly line

This thesis involves the use of simulation models to evaluate and optimize existing manufacturing assembly lines of electrical components. The goal of the simulation is primarily to mimic the existing production scenario in order to identify problematic areas such as bottleneck operations, conveyor speeds limiting production and factors inhibiting the performance of the resources. This simulation project uses a combination of the AweSim ® software and logic programming using MS Visual Basic ® . Through coding, the logic of the flow of the parts is demonstrated in the course of the steps such as the intermittent conveyors with single and double part flow. There are line selection rules in the models that follow restrictions which will affect the makespan, mean flow time and the utilization of the resources. Using different scenarios conclusions and recommendations are made on modifications to the existing production in order to improve makespan and mean flow time.

Digital Twin for Designing and Reconfiguring Human–Robot Collaborative Assembly Lines

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.