An Automated Method to Calibrate Industrial Robots Using a Virtual Closed Kinematic Chain

Gatla, C.S.; Lumia, Ronald; Wood, John; Starr, Gregory P.

doi:10.1109/tro.2007.909765

Cited by 67 publications

(37 citation statements)

References 15 publications

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“…Automatic calibration of a model is addressed by some traditional methods from machine learning [84], system identification [85], [86], or probability theory [87]. More specifically, there is a number of solutions to the automated calibration of a kinematic chain [88]- [90] or a hand/eye setup [91]. Typically, a sampling period in which different configurations are visited is followed by an optimization procedure.…”

Section: ) Fixed Kinematic Modelsmentioning

confidence: 99%

“…The open-loop strategy can be realized through a combination of classical explicit frame of reference transformations that involve the hand, body, and camera reference frames. As mentioned in Section III-C2, these maps can be obtained through automated calibration procedures (kinematic chain [88]- [90], hand/eye setup [91]). However, a highly structured environment is typically required for these calibration procedures (see [118] for more details).…”

Section: ) Representations Of Sensorimotor Mappingsmentioning

confidence: 99%

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Body Schema in Robotics: A Review

Hoffmann

Marques

Arieta

et al. 2010

IEEE Trans. Auton. Mental Dev.

208

101

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Abstract-How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.

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Section: ) Fixed Kinematic Modelsmentioning

confidence: 99%

Section: ) Representations Of Sensorimotor Mappingsmentioning

confidence: 99%

Body Schema in Robotics: A Review

Hoffmann

Marques

Arieta

et al. 2010

IEEE Trans. Auton. Mental Dev.

208

101

View full text Add to dashboard Cite

show abstract

“…Kinematic models are usually derived analytically by a robot engineer and thus rely heavily on prior knowledge about the geometry of the robot. When such a model is applied to a real robot, its parameters have to be carefully calibrated (Gatla et al, 2007) to ensure a high accuracy, for example, using expensive calibration systems at the robot manufacturer's site. As robotic systems become more versatile and are increasingly delivered in completely reconfigurable ways, there is a growing demand for techniques to learn kinematic models automatically.…”

Section: Body Schema Learningmentioning

confidence: 99%

“…When the kinematic model is given in a parametric form, the parameters can be optimized efficiently with respect to an error measure (Gatla et al, 2007;Pradeep et al, 2010;He et al, 2010) or the data likelihood (Roy and Thrun, 1999). Hersch et al (2008) showed that parameter optimization can also be used to adapt the body schema during tool-use, for example, to estimate the tool position and orientation.…”

Section: Related Workmentioning

confidence: 99%

Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Sturm¹

2013

Springer Tracts in Advanced Robotics

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Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots, that implement large parts of the housework, and versatile industrial assistants, that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. To meet these challenges, current robotic systems are typically custom-tailored to specific applications in welldefined environments, and therefore cannot deal robustly with changes in the situation. This thesis presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The developed approaches in this thesis cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations.In the first part of this thesis, we present innovative approaches to learning a robot's own body schema from scratch using visual self-observation. This allows manipulation robots to calibrate themselves automatically and to adapt their body schemata autonomously, for example after hardware failures or during tool use. In the second part, we extend the developed framework to learning about articulated objects -such as doors and drawers -with which service robots often need to interact. The presented algorithms enable robots to learn accurate kinematic models of articulated objects, which in turn allow them to interact with the objects robustly. In the third part, we provide approaches that allow manipulation robots to make use of tactile perception -an ability that is known to play an important role in human object manipulation skills. The main contributions in this part are approaches to identifying objects and to perceiving aspects of their internal states. With this, a manipulation robot can verify that it has grasped the correct object and, for example, discriminate full from empty bottles. Finally, we present an integrated system that allows human operators to intuitively teach a robot novel manipulation tasks by demonstration.All techniques developed in the thesis are based on probabilistic learning and inference. They have been implemented and evaluated on real robots as well as in simulation. Extensive experiments have been conducted to analyze and validate the properties of the developed algorithms and to demonstrate a significant increase in robustness, adaptability, and utility of mobile manipulation robots in everyday life. Zusammenfassung

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“…Gatla et al [16] proposed a new method called "virtual closed kinematic chain". In their method, a laser pointer tool, attached to the end-effector, aims at a constant but unknown location on a fixed distant object, effectively creating a virtual closed kinematic chain.…”

Section: Introductionmentioning

confidence: 99%