A novel calibration method for industrial AGVs

Kallasi, Fabjan; Rizzini, Dario Lodi; Oleari, Fabio; Magnani, Massimiliano; Caselli, Stefano

doi:10.1016/j.robot.2017.04.019

Cited by 14 publications

(14 citation statements)

References 16 publications

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“…These include future trend prediction (Lloyd and Payne, 2017), surgery (Sandoval et al , 2018), medical robotics (M. Nazmul Huda, 2016), humanoid robots (Agravante et al , 2019), rehabilitation robots (Treadway et al , 2018), robotic exo-skeletons (Long et al , 2018; Kazemi and Ozgoli, 2019), pattern recognition systems (Yu and Lee, 2015), tactile sensors (Mastrogiovanni et al , 2015), disaster victim searching (Beck et al , 2018), robot hand design (Feng et al , 2018), industrial automatic guided vehicles (Kallasi et al , 2017), unmanned aerial vehicles (Rajappa et al , 2017), household robots (Mateo Ferrús and Domínguez Somonte, 2016), harvesting applications (Ling et al , 2019), robot in the hotel industry (Pinillos et al , 2016) and entertainment robots (Jochum et al , 2017). These examples illustrate how human–robot collaboration are deployed in different contexts.…”

Section: Methodsmentioning

confidence: 99%

Technology jump in the industry: human–robot cooperation in production

Dobra

Dhir

2020

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Purpose Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research. Design/methodology/approach A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works. Findings The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration. Practical implications This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration. Originality/value This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

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Section: Methodsmentioning

confidence: 99%

Technology jump in the industry: human–robot cooperation in production

Dobra

Dhir

2020

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“…Such a requirement motivates the need for calibration approaches that work without any additional hardware, require no prior information, and do not need to disrupt the operation of the robot. A joint calibration approach for a two-wheel differential drive that is independent of specialized hardware was first proposed in [12], with the formal analysis presented in [13] and has since been extended to other settings such as the tricycle robot [14]. The idea here is to find a maximum likelihood estimate of the various intrinsic and extrinsic parameters (a) Fire Bird VI robot (b) Configuration F1 Fig.…”

Section: Operatorsmentioning

confidence: 99%

“…Henceforth, existing algorithms for joint calibration suffer from two key issues (a) applicability to specific drive configurations due to the use of customized algorithms; and (b) outlier rejection mechanisms requiring either manual intervention or specialized hardware. Besides, extending the approaches in [13,14] to other robot configurations may be possible but not straightforward. More importantly, existing methods do not apply to robots for which full kinematic models are not available.…”

Section: Operatorsmentioning

confidence: 99%

“…Simultaneous calibration of the intrinsic and extrinsic parameters has been considered before, but with the help of external hardware; see, e.g., [40]. As already discussed, the problem of simultaneous calibration without any special equipment was first considered in [12], with the formal analysis presented in [13] for two-wheel differential drive robots and later for tricycle robots in [14]. More generic calibration routines for arbitrary robot configurations were presented in [41] without any convergence guarantees.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Generalized Framework for Autonomous Calibration of Wheeled Mobile Robots

Nutalapati¹,

Arora²,

Bose³

et al. 2020

Preprint

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Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras etc., by providing appropriate transformational relationships between the corresponding reference frames. For wheeled robots equipped with exteroceptive sensors, calibration entails learning the motion model of the sensor or the robot in terms of the odometric data, and must generally be performed prior to performing tasks such as simultaneous localization and mapping (SLAM). Within this context, the current trend is to carry out simultaneous calibration of odometry and sensor without the use of any additional hardware. Building upon the existing simultaneous calibration algorithms, we put forth a generalized calibration framework that can not only handle robots operating in 2D with arbitrary or unknown motion models but also handle outliers in an automated manner. We first propose an algorithm based on the alternating minimization framework applicable to twowheel differential drive. Subsequently, for arbitrary but known drive configurations we put forth an iteratively re-weighted least squares methodology leveraging an intelligent weighing scheme. Different from the existing works, these proposed algorithms require no manual intervention and seamlessly handle outliers that arise due to both systematic and non-systematic errors. Finally, we put forward a novel Gaussian Process-based nonparametric approach for calibrating wheeled robots with arbitrary or unknown drive configurations. Detailed experiments are performed to demonstrate the accuracy, usefulness, and flexibility of the proposed algorithms.

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“…due to nondifferentiability. For such cases, the parametric approaches [10], [11], [17] are no longer feasible and the unknown function f is generally infinite-dimensional. Towards this end, a low-complexity approach is proposed (see Sec.…”

Section: Problem Formulationmentioning

confidence: 99%

Model Free Calibration of Wheeled Robots Using Gaussian Process

Nutalapati

Arora

Bose

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras, etc., by providing appropriate relationships between the corresponding reference frames. For wheeled robots equipped with camera/lidar along with wheel encoders, calibration entails learning the motion model of the sensor or the robot in terms of the data from the encoders and generally carried out before performing tasks such as simultaneous localization and mapping (SLAM). This work puts forward a novel Gaussian Process-based non-parametric approach for calibrating wheeled robots with arbitrary or unknown drive configurations. The procedure is more general as it learns the entire sensor/robot motion model in terms of odometry measurements. Different from existing non-parametric approaches, our method relies on measurements from the onboard sensors and hence does not require the ground truth information from external motion capture systems. Alternatively, we propose a computationally efficient approach that relies on the linear approximation of the sensor motion model. Finally, we perform experiments to calibrate robots with un-modelled effects to demonstrate the accuracy, usefulness, and flexibility of the proposed approach.

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A novel calibration method for industrial AGVs

Cited by 14 publications

References 16 publications

Technology jump in the industry: human–robot cooperation in production

Technology jump in the industry: human–robot cooperation in production

A Generalized Framework for Autonomous Calibration of Wheeled Mobile Robots

Model Free Calibration of Wheeled Robots Using Gaussian Process

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