Collaborative robots for industrial tasks: A review

Bisen, Asmita Singh; Payal, Himanshu

doi:10.1016/j.matpr.2021.09.263

Cited by 9 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper also points out the research gaps and proposes solutions to bridge them. Finally, the identification of trends in Cobots has been discerned and future scope of developments has been stipulated (4) .…”

Section: Literaturementioning

confidence: 99%

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Saleemuddin,

Hudgikar

2024

IJST

View full text Add to dashboard Cite

Objectives: This study aims to enhance cellular manufacturing systems by optimizing cobot and tool assignments, maximizing flexibility, and minimizing production time, workload imbalances, energy consumption, error rates and rework. Methods: This study employs a sophisticated multi-objective optimization approach, integrating constraints into the cellular manufacturing system using advanced linear or integer programming techniques. The model is designed to dynamically adapt in real-time, allowing for flexibility in response to evolving production needs. We systematically evaluate cobot and tool assignments, balancing conflicting objectives within a comprehensive mathematical framework. The optimization process is fine-tuned to consider machine capacities, part type assignments, and tool compatibility, ensuring the practicality and realism of the proposed solutions. The overarching goal is to identify optimal configurations that minimize production time, workload imbalances, energy consumption, error rates and rework while maximizing system adaptability. Findings: The optimal cobot and tool assignments, determined through the multi-objective optimization model, yielded substantial improvements across critical metrics compared to a scenario without cobots. This data showcases a 26% reduction in production time, a 20% decrease in workload imbalance, a 20% improvement in flexibility, a 28% reduction in energy consumption, and a 26% decrease in error rates and rework when utilizing the proposed multi-objective optimization approach. These tangible improvements underscore the practical benefits of integrating cobots in cellular manufacturing systems.Top of Form Novelty: This study introduces a novel multi-objective optimization approach for cellular manufacturing, enhancing adaptability and efficiency through strategic cobot and tool assignments. Keywords: Cellular Manufacturing Systems, Cobots, Tool Assignment, MultiObjective Optimization, Production Time, Workload Balancing, Energy Consumption, Error Rates, Flexibility

show abstract

Section: Literaturementioning

confidence: 99%

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Saleemuddin,

Hudgikar

2024

IJST

View full text Add to dashboard Cite

show abstract

“…Unfortunately, robots are most often relegated to cages and isolated sections of manufacturing sites because of the inherent danger they present to human operators through their fast-moving, heavy, and rigid structures. Efforts toward allowing these robots to perform safely with human collaborators have focused on software control, but absolute guarantees of safety are not possible (3)(4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

Control of soft robots with inertial dynamics

Haggerty,

Banks,

Kamenar

et al. 2023

Sci. Robot.

View full text Add to dashboard Cite

Soft robots promise improved safety and capability over rigid robots when deployed near humans or in complex, delicate, and dynamic environments. However, infinite degrees of freedom and the potential for highly nonlinear dynamics severely complicate their modeling and control. Analytical and machine learning methodologies have been applied to model soft robots but with constraints: quasi-static motions, quasi-linear deflections, or both. Here, we advance the modeling and control of soft robots into the inertial, nonlinear regime. We controlled motions of a soft, continuum arm with velocities 10 times larger and accelerations 40 times larger than those of previous work and did so for high-deflection shapes with more than 110° of curvature. We leveraged a data-driven learning approach for modeling, based on Koopman operator theory, and we introduce the concept of the static Koopman operator as a pregain term in optimal control. Our approach is rapid, requiring less than 5 min of training; is computationally low cost, requiring as little as 0.5 s to build the model; and is design agnostic, learning and accurately controlling two morphologically different soft robots. This work advances rapid modeling and control for soft robots from the realm of quasi-static to inertial, laying the groundwork for the next generation of compliant and highly dynamic robots.

show abstract

“…Compared with traditional industrial robots, collaborative robots have the benefits of high security, good versatility, sensitivity, precision, ease of use, and human-machine collaboration. The above advantages make collaborative robots not only applicable in the manufacturing field, but also gives them potential application value in the fields of home service and rehabilitation medicine-for example, compliant robotic arms in the industrial field, surgical robots in the medical field, wearable rehabilitation assistance robots, and antiterrorist and explosion-proof robots in special applications [1,2]. Utilizing the technology of human-machine fusion, the establishment of a fusion robot technology with intrinsic safety, human-machine collaborative cognition, and behavioral mutual assistance can provide support for emerging new application scenarios such as industry, service, and medical care.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Tang

Yan

et al. 2023

Machines

View full text Add to dashboard Cite

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

show abstract

Collaborative robots for industrial tasks: A review

Cited by 9 publications

References 8 publications

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Optimizing Cellular Manufacturing Systems Through Multi-Objective Cobot Coordination and Tool Allocation

Control of soft robots with inertial dynamics

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Contact Info

Product

Resources

About