Proteomics analysis identifies PEA-15 as an endosomal phosphoprotein that regulates α5β1 integrin endocytosis

Endosomal trafficking of cell surface receptors is essential to their function. Integrins are transmembrane receptors that integrate adhesion to the extracellular matrix with engagement of the cytoskeleton. Ligated integrins mediate diverse signals that regulate matrix assembly, cell survival, cell morphology, and cell motility. Endosomal trafficking of integrins modulates these signals and contributes to cell motility and is required for cancer cell invasion. The phosphoprotein PEA-15 modulates integrin activation and ERK MAP Kinase signaling. To elucidate novel PEA-15 functions we utilized an unbiased proteomics approach. We identified several binding partners for PEA-15 in the endosome including clathrin and AP-2 as well as integrin β1 and other focal adhesion complex proteins. We confirmed these interactions using proximity ligation analysis, immunofluorescence imaging, pull-down and co-immunoprecipitation. We further found that PEA-15 is enriched in endosomes and was required for efficient endosomal internalization of α5β1 integrin and cellular migration. Importantly, PEA-15 promotion of migration was dependent on PEA-15 phosphorylation at serines 104 and 116. These data support a novel endosomal role for PEA-15 in control of endosomal trafficking of integrins through an association with the β1 integrin and clathrin complexes, and thereby regulation of cell motility.

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

Purpose The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments. Design/methodology/approach The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized. Findings The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches. Research limitations/implications The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions. Originality/value The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

Seamless Human–Robot Collaborative Assembly Using Artificial Intelligence and Wearable Devices

Seamless human–robot collaboration requires the equipping of robots with cognitive capabilities that enable their awareness of the environment, as well as the actions that take place inside the assembly cell. This paper proposes an AI-based system comprised of three modules that can capture the operator and environment status and process status, identify the tasks that are being executed by the operator using vision-based machine learning, and provide customized operator support from the robot side for shared tasks, automatically adapting to the operator’s needs and preferences. Moreover, the proposed system is able to assess the ergonomics in human–robot shared tasks and adapt the robot pose to improve ergonomics using a heuristics-based search algorithm. An industrial case study derived from the elevator manufacturing sector using a high payload collaborative robot is presented to demonstrate that collaboration efficiency can be enhanced through the use of the discussed system.

A New Self-Balancing Assembly Line Based on Collaborative Ant Behavior

In most mass-production assembly lines, workers perform a set of tasks repetitively predefined by assembly line balancing techniques. The static task assignment often leads to low productivity when the assembly system faces disruptions or uncertainties such as machine breakdown and uneven worker capabilities. The idea of bucket brigades (BB) has been introduced to address the static assignment problems where cooperative behavior of ants is applied to flow line control. This paper examines possible efficiency losses associated with the existing BB-based assembly cell and presents an improved version for assembly cells under uncertain environments. The new system attempts to enhance productivity by assigning assembly tasks to workers dynamically and possibly adding buffers for decoupling consecutive workers. The proposed assembly system is evaluated through simulation experiments under various manufacturing environments. The experimental results show that the new system provides higher productivity than the naïve BB-based assembly cell as well as traditional assembly cells, especially for uncertain assembly environments.

An Agent-Based System for Automated Configuration and Coordination of Robotic Operations in Real Time—A Case Study on a Car Floor Welding Process

This paper investigates the feasibility of using an agent-based framework to configure, control and coordinate dynamic, real-time robotic operations with the use of ontology manufacturing principles. Production automation agents use ontology models that represent the knowledge in a manufacturing environment for control and configuration purposes. The ontological representation of the production environment is discussed. Using this framework, the manufacturing resources are capable of autonomously embedding themselves into the existing manufacturing enterprise with minimal human intervention, while, at the same time, the coordination of manufacturing operations is achieved without extensive human involvement. The specific framework was implemented, tested and validated in a feasibility study upon a laboratory robotic assembly cell with typical industrial components, using real data derived from a car-floor welding process.