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Proceedings ArticleDOI

A service robot for automating the sample management in biotechnological cell cultivations

TL;DR: A mobile robot system that is capable of automating the sample management in a biotechnological laboratory and specified in simple scripts to allow quick and easy adaptations to other situations is presented.
Abstract: In this paper we present a mobile robot system that is capable of automating the sample management in a biotechnological laboratory. The system consists of a mobile platform and a robot arm. It can navigate freely in the laboratory and operate standard devices needed for the sample management. The platform uses an extended Kalman filter for localization and A* algorithm for path planning on a tangent graph computed from the laboratory's map. Motion execution has been designed to be as predictable as possible to not irritate, disturb or harm human personnel. The robot arm uses color vision to detect devices and compensate for positioning errors. The parameters and tasks needed to operate the devices are specified in simple scripts to allow quick and easy adaptations to other situations.

Summary (3 min read)

Introduction

  • Its particular importance lies in monitoring the culture and determining the optimal harvest time.
  • These machines are both expensive and inflexible.
  • Instead, a steam-sterilizable sampling system directly connected to the bioreactor is used to fill a sample into a tube, which is then carried to the different analytical devices by the robot.
  • The drive wheels and the gyro are connected to the PC via CAN bus.
  • The basis for the successful manipulation of the devices with the robot system is a precise positioning of the mobile platform, for which three prerequisites have to be met:.

A. Localization

  • Experiments done by Gutmann et. al. in [7] and [8] show that Kalman filtering techniques yield the most precise results for solving the localization problem.
  • The system-state vector~xt contains the platform’s position and velocity and the angular velocities of the drive wheels as well as the positions of the laser reflector marks.
  • These reflector marks have to be distributed throughout the mobile robot’s workspace and serve as landmarks with a known global position.
  • Apart from the state vector the EKF also uses a measurement vector~zt containing all available sensor information.
  • In addition the rotational velocity of the complete mobile platform, made available by the gyro compass, is part of the measurement vector.

B. Navigation

  • In order to solve the navigation problem, theA∗ algorithm and tangent graph discussed by Latombe in [10] have been applied for planning a path from the current position to the desired goal position at a device.
  • TheA∗ algorithm searches for the shortest path on a tangent graph built on a set of polygons.
  • These polygons represent the static obstacles in the mobile platform’s workspace and are generated by expanding a simple vector representation of the laboratory’s map.
  • Based on that, a tangent graph is that set of straight lines which connect all polygon vertices without going through any polygon body.
  • The three obstacles are expanded by the radius of a circle which surrounds the robot.

C. Motion Execution

  • Motion execution does not utilize any behaviours (like “avoid obstacles”) but strictly follows the computed path.
  • This may seem inflexible, but makes the platform predictable and verifiable - features which are of great importance in an environment with human presence in which a machine has to meet several safety standards.
  • This does allow overshooting of the mobile platform along the trajectory, but has the advantage of keeping the platform from getting trapped in a potentially endless loop of trying to come within a small catch radius around the target with a desired orientation.
  • This compensation is done with the help of visual fine-positioning, which is discussed in the next section.

A. Color Vision

  • Cameras provide the most natural and comprehensive information.
  • They require a very large amount of processing to extract the essential information.
  • Since their objects either have colored regions or can be easily tagged with a colored label the authors chose to employ a color-based approach to detect objects by searching for known colors.
  • If you are looking at “color” technically the often used RGB color representation has the disadvantage that it mixes color and brightness information.
  • Its only disadvantage is that the color information in a YUV image is encoded with less bandwidth than brightness information, and therefore has a lower signal- noise-ratio.

B. Model Matching

  • These regions are reduced to their color andcenter of gravity (COG) to build a model of the image scene, which is matched against stored models of objects.
  • Learning-based approaches usually only perform correctly if the learn space is homogeneously covered by learn data.
  • As can be seen in Fig. 9, this approach does not deal with perspective effects caused by displacements and/or lens errors.
  • LSE allows an unambiguous classification of different devices with different displacements.
  • Only if the scene should become so dark that complete regions are lost the classification will fail.

A. Sequence Scripts

  • The sequence of commands and parameters - mostly homogeneous transformations describing spatial relationships - required for each high-level function is stored in a central database and is re-read each time the function is invoked.
  • Since they are stored as ASCII text they can be trivially changed.
  • It has proven to be impossible to use an existing script language/interpreter (like tcl/tk, perl, python etc.) and have all the functionality in the script.
  • This is because of the need to access hardware and/or to have realtime capabilities, e.g. for force control.
  • This lack of complexity in the script language in turns allows people with comparably little training to do changes.

B. Script Commands

  • Script commands offer textual access to routines implemented in C++ in the main program.
  • They represent a simplified approach to the full functionality of RCCL [1] for arm control, the vision system and the mobile platform.
  • Only those aspects needed to allow easy adaptation are used in each case.

C. High-Level Functions

  • With these script commands the set of high-level functions is realized.
  • A sequence of functions that meets the specific biotechnological requirements for sample management can then be issued by the process control system.

D. State Machine

  • The high-level functions are secured by a state machine using attributes to describe the system state.
  • This state machine enforces checks which ensure that no damage is done to the system in case of accidental mixing up of the command order.
  • Axel Schneider and Daniel Westhoff (2002),“Autonomous Navigation and Control of a Mobile Robot in a Cell Culture Laboratory”, Diploma Thesis, Faculty of Technology, University of Bielefeld, POB 100131, D-33501 Bielefeld, Germany. [4].
  • A. Hawerkamp (2001),“Automated Trypan Blue Method for Optimal Cell Viability Determination”, American Biotechnology Laboratory Feb. 2001. [13].

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Citations
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Journal ArticleDOI
TL;DR: An assembly line paradigm that exploits the latest trends in handling and assembly technologies is described, using mobile robotic units, flexible and exchangeable end effectors, equipped with automated integration capabilities to achieve a highly reconfigurable and autonomous line.
Abstract: The aim of this paper is to describe an assembly line paradigm that exploits the latest trends in handling and assembly technologies. Mobile robotic units, flexible and exchangeable end effectors, equipped with automated integration capabilities are used to achieve a highly reconfigurable and autonomous line as opposed to the existing fixed assembly lines, which are difficult to be reconfigured. In a study for the automotive assembly case, the benefits of the new paradigm over the traditional assembly schemes are quantified with the help of different simulated operational conditions.

38 citations

01 Jan 2004
TL;DR: Schlieslich et al. as discussed by the authors prasentiert ein neuer Serviceroboter, der aus einem auf einer mobilen Plattform montierten Roboterarm besteht and diese Lucke schliest.
Abstract: In biotechnologischen Laboratorien ist die Qualitat der typischerweise pharmazeutischen Produkte ein wortwortlich lebenswichtiges Ziel. Die Qualitat der Zellkultivierungen wurde historisch nur durch off-line Messungen von physikalischen Prozessparametern wie pH und pO2 sichergestellt. Biologische Parameter wie die Zelldichte und -viabilitat wurden nur off-line gemessen, weil das dazu notwendige Probenmanagement hochkomplizierte Manipulationen und Analysen beinhaltet und deshalb nicht automatisiert werden konnte. Es gibt zwar mehrere automatisierte Gerate, um einem Labortechniker zu assistieren, aber kein System, welches das gesamte Probenmanagement automatisiert. In dieser Arbeit wird ein neuer Typ von Serviceroboter prasentiert, der aus einem auf einer mobilen Plattform montierten Roboterarm besteht und diese Lucke schliest. Dieser Roboter muss eine ganze Reihe von Problemen bewaltigen: Er muss seine Position im Labor bestimmen konnen (Lokalisation), er muss eine kollisionsfreie Bahn zu den beteiligten Geraten finden konnen (Bahnplanung mit Hindernisvermeidung), er darf bei seinen Bewegungen keine Menschen gefahrden oder Laborausrustung beschadigen (Kollisionsvermeidung), er muss die zu bedienenden Gerate erkennen und ihre Position prazise messen konnen (Bildverarbeitung), er muss sie bedienen konnen (Armsteuerung), er muss Objekte greifen konnen (Greifer und Finger) und er muss sie gefugig handhaben konnen, um sie nicht zu beschadigen (Kraftregelung). Er muss autonom sein, um nur die allernotwendigste Menge an Benutzereingriffen zu benotigen, und doch durch ein Laborsteuerprogramm kontrollierbar sein, um Eingriffe zu erlauben. Schlieslich muss er einfach durch ungeschultes Personal zu warten sein. All diese Aspekte werden von dem in dieser Arbeit prasentierten neuen Robotersystem abgedeckt.

22 citations

Proceedings ArticleDOI
12 Nov 2007
TL;DR: The actual stage in the development of a mobile robot assistant that is going to be capable of safe cooperation with humans in a populated environment is presented and the approaches to object recognition and intuitive multimodal human-machine interaction using speech and touchpad input are described.
Abstract: In this paper we present the actual stage in the development of a mobile robot assistant that is going to be capable of safe cooperation with humans in a populated environment. The service robot is intended to assist users in biological and pharmaceutical laboratories by carrying out routine jobs such as filling and transportation of microplates. Relevant safety requirements are outlined and a safety concept is devised, which consists of various sensor systems such as laser scanners, thermographic components and artificial skin. An overview of the design of the mobile platform with a robotic arm is provided. Moreover, the approaches to object recognition and intuitive multimodal human-machine interaction using speech and touchpad input are described. All aspects are regarded concerning safety since the robot and humans share a common environment and interact closely.

12 citations


Additional excerpts

  • ...Another application for a scenario similar to that of the LiSA project is the mobile robot for automated cell cultivation developed at the University of Bielefeld [9]....

    [...]

01 Jan 2004
TL;DR: A technology that establishes a framework for task-oriented programming of mobile robot systems that allows writing distributed control or monitoring programs for easy adoption of robots to specific tasks and hides all network details from the programmer.
Abstract: In this paper we present a technology that establishes a framework for task-oriented programming of mobile robot systems. The framework allows writing distributed control or monitoring programs for easy adoption of robots to specific tasks. It enables the programmer to send programs referred to as Roblets® to a Roblet®-server running on the robot. Contrary to other distributed system frameworks these Roblets® consist of data and code. The Roblet®-server executes the Roblets® with well-defined behaviour, even in case of malfunctions. The framework hides all network details from the programmer, so that writing programs on a local computer is similar to working directly with the remote robot. This decreases the development time of programs controlling the robots. The framework is implemented in JavaTM and tested with two service robots.

10 citations


Cites background from "A service robot for automating the ..."

  • ...The following subsections discuss some of the problems of service robot systems....

    [...]

Journal ArticleDOI
TL;DR: The clinical and analytical laboratory has been at the forefront of the fight against the COVID-19 pandemic, where testing is carried out, guiding hospitalization and providing information on the spread of the disease.
Abstract: The clinical and analytical laboratory has been at the forefront of the fight against the COVID-19 pandemic. This is where testing is carried out, guiding hospitalization and providing information on the spread of the disease. It is also central to the development of new medicines and the vaccines that are still being tested. Laboratories make extensive use of robotics and automation to safely improve quality and productivity at an acceptable cost.

9 citations


Cites background from "A service robot for automating the ..."

  • ...) Interest from the robotics research community resulted in some promising ideas [3], [4] and continues at the cutting edge [5], [6]....

    [...]

References
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TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Abstract: 1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.

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TL;DR: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction and the resulting methods are accurate, noise resistant and fast.
Abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.

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"A service robot for automating the ..." refers background in this paper

  • ...This lack of complexity in the script language in turns allows people with comparably little training to do changes....

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Proceedings ArticleDOI
13 Oct 1998
TL;DR: This experimental study compares two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which using Kalman filtering techniques based on matching sensor scans.
Abstract: Localization is the process of updating the pose of a robot in an environment, based on sensor readings. In this experimental study, we compare two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which uses Kalman filtering techniques based on matching sensor scans. Both these techniques are dense matching methods, that is, they match dense sets of environment features to an a priori map. To arrive at results for a range of situations, we utilize several different types of environments, and add noise to both the dead-reckoning and the sensors. Analysis shows that, roughly, the scan-matching techniques are more efficient and accurate, but Markov localization is better able to cope with large amounts of noise. These results suggest hybrid methods that are efficient, accurate and robust to noise.

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Proceedings ArticleDOI
10 Dec 2002
TL;DR: Experimental evidence is given that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.
Abstract: Localization is one of the fundamental problems in mobile robot navigation. Past experiments have shown that, in general, grid-based Markov localization is more robust than Kalman filtering while the latter can be more accurate than the former Recently new methods for localization employing particle filters have become popular. In this paper, we compare different localization methods using Kalman filtering, grid-based Markov localization, Monte Carlo Localization (MCL), and combinations thereof. We give experimental evidence that a combination of Markov localization and Kalman filtering as well as a variant of MCL outperform the other methods in terms of accuracy, robustness, and time needed for recovering from manual robot displacement, while requiring only few computational resources.

216 citations


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TL;DR: Linear estimation theory is applied to Kalman filtering for the alighnment of carrier aircraft inertial navigation systems and navigation at sea using the invariants form of Kalman filters.
Abstract: : Contents: Linear estimation theory; Further comments on the derivation of Kalman filters; Computational techniques in Kalman filtering; Modeling errors in Kalman filters; Suboptimal Kalman filter techniques; Comparison of Kalman, Bayesian and maximum likelihood estimation techniques; Nonlinear filtering and comparison with Kalman filtering; Linear smoothing techniques (post-flight data analysis); Nonlinear smoothing techniques; General questions on Kalman filtering in navigation systems; Application of Kalman filtering theory to augmented inertial navigation systems; Application of Kalman filtering to Baro/inertial height systems; Application of Kalman filtering to the C-5 guidance and control system; Application of Kalman filtering techniques to the Apollo program; Some applications of Kalman filtering in space guidance; Application of Kalman filtering for the alighnment of carrier aircraft inertial navigation systems; Navigation at sea using the invariants form of Kalman filtering; Marine applications of Kalman filtering; Optimal use of redundant information in an inertial navigation; Application of Kalman filtering techniques to strapdown system initia-alignment; and A Kalman filter augmented marine navigation system.

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"A service robot for automating the ..." refers background or methods in this paper

  • ...[9] S. Schmidt (1970) ,“Computational techniques in kalman filtering”, in “Theory and Applications of Kalman Filtering”, AGARDograph 139, NATO Advisory Group for Aerospace Research and Development, London....

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  • ...If the platform encounters an obstacle it stops and waits for it to move or to be moved away (after notifying a human operator, if necessary)....

    [...]

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In this paper the authors present a mobile robot system that is capable of automating the sample management in a biotechnological laboratory.