The decision-making framework for assembly tasks planning in human–robot collaborated manufacturing system

Ren, Weibo; Yang, Xiaonan; Yan, Yan; Hu, Yaoguang

doi:10.1080/0951192x.2022.2081359

Cited by 8 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation ( 6) ensures that at any given time, the number of production orders processed by the same process does not exceed the number of machines available for that process. Finally, equation (7) establishes that the next production order can only be selected after the previous production order has selected its machine.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…These systems offer advantages such as easier access to updated drawings, the ability to quickly share changes or corrections, and improved collaboration among different parts of the manufacturing team. Reasonable task allocation methods can optimize the allocation of production resources, thereby significantly improving the economic and social benefits of enterprises [6][7][8][9]. With the increasing level of informatization and automation, the intelligent distribution of manufacturing drawings has become an inevitable trend.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Wang,

Cai,

Huang

et al. 2024

Electronics

View full text Add to dashboard Cite

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information and communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported Industry 4.0 models due to a lack of technical expertise and infrastructure. These enterprises commonly encounter variable production volumes, differing priorities in customer orders, and diverse production capacities across low-, medium-, and high-level outputs. Frequent issues with machine health, glitches, and major breakdowns further complicate optimizing production scheduling. This paper presents a novel production management approach that harnesses bio-inspired methods alongside Internet of Things (IoT) technology to address these challenges. This comprehensive approach integrates the real-time monitoring and intelligent production order distribution, leveraging advanced LoRa wireless communication technology. The system ensures efficient and concurrent data acquisition from multiple sensors, facilitating accurate and prompt capture, transmission, and storage of machine status data. The experimental results demonstrate significant improvements in data collection time and system responsiveness, enabling the timely detection and resolution of machine failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on machine status, effectively reducing production completion time and machine idle time. Case studies in a screw manufacturing facility validate the practical applicability and effectiveness of the proposed system. The seamless integration of the scheduling algorithm with the real-time monitoring subsystem ensures a coordinated and efficient production process, ultimately enhancing productivity and resource utilization. The proposed system’s robustness and efficiency highlight its potential to revolutionize production management in small-scale manufacturing settings.

show abstract

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Wang,

Cai,

Huang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…The service districts served by service stations 1, 50, 12, and 122, are not contiguous, and service units 39, 40, 137, and 171 are served by the corresponding service stations across other service districts. In reality, the cross-regional maintenance service shown in Figure 8 is not generally a feasible solution in agriculture, which results in a long service latency and resource waste [5]. In agriculture, the decision-maker may prefer to assign these service units to the nearest service stations, such as allocating service unit 39 to service station 50, service unit 40 to service station 43, service unit 137 to service station 138, and service unit 171 to service station 173.…”

Section: Analysis Of Contiguity Constraintsmentioning

confidence: 99%

“…Agricultural machinery is prone to failure due to high temperatures, complicated environments, and long-term agricultural operations, especially in the busy season [2,3]. To maintain stable production in agriculture, manufacturers always design a maintenance service network to supply maintenance for failed machinery [4,5].…”

Section: Introductionmentioning

confidence: 99%

Joint Location–Allocation Model for Multi-Level Maintenance Service Network in Agriculture

Li,

Ren,

Wang

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters.

show abstract

“…To guarantee a high level of flexibility in the planning and execution of collaborative tasks, we deploy a hierarchical Task and Motion Planning framework that allows for online planning of the robot trajectories according to the user and the environment's state. This is key to ensuring a smooth collaboration between the human and the robot because the robot's tasks can be robust with respect to changes in objects and tools' positions, and the robot's movement can be optimized to avoid interference with the user's activities [40].…”

Section: Integrated Task and Motion Planningmentioning

confidence: 99%

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

et al. 2022

View full text Add to dashboard Cite

Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The coexistence of human and autonomous robotic agents raises several methodological and technological challenges for the design of effective, safe, and reliable control paradigms. This work proposes the integration of novel technologies from Artificial Intelligence, Control and Augmented Reality to enhance the flexibility and adaptability of collaborative systems. We present the basis to advance the classical human-aware control paradigm in favor of a user-aware control paradigm and thus personalize and adapt the synthesis and execution of collaborative processes following a user-centric approach. We leverage a manufacturing case study to show a possible deployment of the proposed framework in a real-world industrial scenario.

show abstract

The decision-making framework for assembly tasks planning in human–robot collaborated manufacturing system

Cited by 8 publications

References 44 publications

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Joint Location–Allocation Model for Multi-Level Maintenance Service Network in Agriculture

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

Contact Info

Product

Resources

About