Comparison Between Genetic Fuzzy Methodology and Q-Learning for Collaborative Control Design

Sathyan, Anoop; Cohen, Kelly; Ma, Ou

doi:10.5121/ijaia.2019.10201

Cited by 6 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the use of machine learning in diverse industries and warehouses has increased recently [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43], the COVID-19 pandemic has served as a warning call. Some businesses halted operations to reduce the risk of infection among workers on assembly lines in close proximity to one another.…”

Section: Artificial Intelligencementioning

confidence: 99%

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

et al. 2023

View full text Add to dashboard Cite

A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research.

show abstract

Section: Artificial Intelligencementioning

confidence: 99%

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Such GFSs have proven to be extremely successful in various applications including task assignment and planning (Sathyan et al, 2016 ), simulated air-to-air combat (Ernest et al, 2016 ), athlete movement prediction (Sathyan et al, 2019b ), etc. GFS framework was also used for our previous work on a problem where the robots were placed along a regular polygon with the same objective of bringing the common effector to any arbitrarily defined position within the polygon (Sathyan and Ma, 2018 , 2019 ; Sathyan et al, 2018 , 2019a ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

Sathyan

Cohen

2020

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

show abstract

Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Sathyan

Cohen

2021

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

Comparison Between Genetic Fuzzy Methodology and Q-Learning for Collaborative Control Design

Cited by 6 publications

References 23 publications

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Contact Info

Product

Resources

About