Human-Robot Cooperative Interaction Control for the Installation of Heavy and Bulky Components

Roveda, Loris; Castaman, Nicola; Ghidoni, Stefano; Franceschi, Paolo; Boscolo, Nicol; Pagello, Enrico; Pedrocchi, Nicola

doi:10.1109/smc.2018.00067

Cited by 20 publications

(15 citation statements)

References 19 publications

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“…-1) Simplified risk analysis based on historically occupied regions 2) Planning algorithms to perform optimal human-robot movements on shared environments 3) AR assistance for task developing or robot programming [160]- [163] [130], hybrid cells for simultaneous assembly between an operator and a robot [55], [131], [132], use of augmented reality as a support system for operators on a collaborative assembly [133], dual-arm cooperative and flexible assembly [134] and the installation of heavy and bulky components [135]. There are also support activities to assembly task as a chaotic bin-picking task for low volume assemblies [136].…”

Section: Allowedmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shop-floor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.

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Section: Allowedmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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“…ey also describe a human-robot cooperation methodology to install heavy and bulky components based on marker-based visual servoing and force control [30]. erefore, the stable control system for robotic floor tiling needs an in-depth study.…”

Section: Robot Floor-tiling Control Strategymentioning

confidence: 99%

Robot Floor‐Tiling Control Method Based on Finite‐State Machine and Visual Measurement in Limited FOV

Wang

Zhou

Zhang

et al. 2021

Advances in Civil Engineering

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In recent years, accelerative aging society is meeting the short supply of young and middle-aged labor. Particularly, most young people are reluctant to work in the construction industry, which has caused the labor cost of floor tiling to rise year by year. In addition, floor tiling requires workers to continuously bend over or lean over to work, which greatly jeopardizes the physical health of them. Therefore, advanced technology applied in floor tiling is highly demanded to replace the traditional manual method. On the context, the automatic method of floor tiling may promote the transformation and upgrading of the industry. Although a few robots for floor tiling have been developed, the automation of existing systems is still at a low level. This paper proposes a robot floor-tiling control strategy based on visual measurement feedback and finite-state machine, in which the calculation of tile position information in limited field of vision is obtained by an improved Canny edge detection and a Hough linear transformation. Moreover, an algorithm for complementing tile position information based on visual measurement is proposed, and the quality of tile laying is evaluated online. To evaluate the effect of the proposed control strategy, the experimental verifications are given. The experimental results indicate that the proposed method can complete the automatic floor tiling with high accuracy.

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“…Such an assumption is common in many real tasks (such as insertions, assemblies, etc.) where no reorientation of the robot end-effector is required, or when a bulky and/or heavy payload is manipulated and rotation path is pre-calculated (i.e, defining a reference trajectory for the robot) [9,49] (see https://www.youtube.com/watch? v=mdYpZZw93YE).…”

Section: Cartesian Impedance Controlmentioning

confidence: 99%

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Roveda

Maskani

Franceschi

et al. 2020

J Intell Robot Syst

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131

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Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human's fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved humanrobot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system.

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Human-Robot Cooperative Interaction Control for the Installation of Heavy and Bulky Components

Cited by 20 publications

References 19 publications

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Robot Floor‐Tiling Control Method Based on Finite‐State Machine and Visual Measurement in Limited FOV

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

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