The design of a semi-automated football table

Janssen, Rob; Best, Jeroen de; Molengraft, René van de; Steinbuch, M Maarten

doi:10.1109/cca.2010.5611314

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…A schematic overview of the EUTAFT is shown in Figure 2 [13]. The four rods on one side of the table are connected to two motors each, respectively providing the rod's translation and rotation.…”

Section: A System Overviewmentioning

confidence: 99%

“…To overcome this problem, the projective invariant cross-ratio is used together with knowledge of the invariants as discussed in Section II-D2. The cross-ratio between the four detected points of the two frames shown in Figure 6 is calculated in (13), where v a , v b , v c , and v d represent the pixel positions of the four indicated puppets.…”

Section: Initializationmentioning

confidence: 99%

“…In [23] an overhead camera is used for tracking, which was later replaced by a camera looking from underneath a transparent playing field [22]. The overhead camera approach was adapted by [2] and [13]. The commonality between these systems is that the approach is developed specific for one fixed system;…”

Section: Introductionmentioning

confidence: 99%

“…This work continues on the development of the Eindhoven University of Technology Autonomous Football Table (EUTAFT) in [13]. Since this approach is so parameterdependent, a calibration is necessary every time a parameter is changed.…”

Section: Introductionmentioning

confidence: 99%

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Invariant-Based World Models for Robust Robotic Systems Demonstrated on an Autonomous Football Table

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Bruyninckx

et al. 2022

IEEE Robot. Autom. Lett.

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This work explains the use of invariants in robotic perception and control skills. An "invariant" is a mathematical constraint that remains unchanged under a particular transformation in a system. This property makes robotic functionalities more robust against "disturbances" that cause these transformation. These invariants are stored in a world model (WM), which has a central role in the information architecture of the robot to share information between components. A "robotic" football table serves as an example to illustrate the effectiveness of invariants. Despite looking different, all standard football tables satisfy the same set of invariants; the common layout of the field, line markings and puppets, which are not identical but satisfy the same set of constraints, such as parallelism, partial ordering, and relative colour and intensity differences between objects. During initialization, the invariants are actively identified and saved to the world model. During game play this world model is used by the perception and action skills and is updated when necessary. This work shows how the use of invariants creates robustness against variation in placement of the perception system and against variation in table dimensions and colours. When the camera is misaligned or moved mid-play, the world model is updated to ensure a smooth continuation of the game. The approach is tested on three standard football tables, with different dimensions and colours, showing that the approach is robust on standard tables that adhere to the invariants.

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Section: A System Overviewmentioning

confidence: 99%

Section: Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Invariant-Based World Models for Robust Robotic Systems Demonstrated on an Autonomous Football Table

Senden

Jebbink

Bruyninckx

et al. 2022

IEEE Robot. Autom. Lett.

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“…Then, the contributions [5][6][7][8] deal with adaptive, constrained, and optimal robot control. Lastly, the contributions [9][10][11][12] consider visual servoing with manipulators and mobile robots.…”

mentioning

confidence: 99%

Special Issue on Visual Servoing

Chesi¹,

Hashimoto²,

Prattichizzo³

2012

Mechatronics

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Robots able to imitate human beings have been at the core of stories of science fiction as well as dreams of inventors since long time. Visual servoing has received great attention in recent years as it enables robotic systems to imitate humans in positioning tasks by exploiting artificial vision. Indeed, visual servoing has numerous and various applications, such as industrial manufacture, surveillance, vehicles control, operations in dangerous environments, motion control, and surgery. Pioneering visual servoing approaches focused their attention on simple images features and control laws in order to result in low computational complexities and hence being suitable for real-time applications. Recently, developments in system hardware and optimization algorithms make efficient and viable the exploitation of sophisticated techniques in visual servoing, thus allowing to consider complex scenarios and derive powerful solutions in many applications.

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