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The Carologistics team participates in the RoboCup Logistics League for the seventh year. The RCLL requires precise vision,
manipulation and path planning, as well as complex high-level decision
making and multi-robot coordination. We outline our approach with an
emphasis on recent modifications to those components.
The team members in 2018 are David Bosen, Christoph Gollok, Mostafa
Gomaa, Daniel Habering, Till Hofmann, Nicolas Limpert, Sebastian Schönitz,
Morian Sonnet, Carsten Stoffels, and Tarik Viehmann.
This paper is based on the last year’s team description.
In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human–robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents.
In the future, we expect manufacturing companies to follow a new paradigm that mandates more automation and autonomy in production processes. Such smart factories will offer a variety of production technologies as services that can be combined ad hoc to produce a large number of different product types and variants cost-effectively even in small lot sizes. This is enabled by cyber-physical systems that feature flexible automated planning methods for production scheduling, execution control, and in-factory logistics.
During development, testbeds are required to determine the applicability of integrated systems in such scenarios. Furthermore, benchmarks are needed to quantify and compare system performance in these industry-inspired scenarios at a comprehensible and manageable size which is, at the same time, complex enough to yield meaningful results.
In this chapter, based on our experience in the RoboCup Logistics League (RCLL) as a specific example, we derive a generic blueprint for how a holistic benchmark can be developed, which combines a specific scenario with a set of key performance indicators as metrics to evaluate the overall integrated system and its components.
In this paper we present an extension of the action language Golog that allows for using fuzzy notions in non-deterministic argument choices and the reward function in decision-theoretic planning. Often, in decision-theoretic planning, it is cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, even for domain experts, it is not always easy to specify a reward function. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in Golog, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy uents. In Golog’s forward-search DT planning algorithm, these formulas are evaluated in order to find the agent’s optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
20 Years of RoboCup
(2016)
This summer, RoboCup competitions were held for the 20th time in Leipzig, Germany. It was the second time that RoboCup took place in Germany, 10 years after the 2006 RoboCup in Bremen. In this article, we give an overview on the latest developments of RoboCup and what happened in the different leagues over the last decade. With its 20th edition, RoboCup clearly is a success story and a role model for robotics competitions. From our personal view point, we acknowledge this by giving a retrospection about what makes RoboCup such a success.
Robots are widely used as a vehicle to spark interest in science and technology in learners. A number of initiatives focus on this issue, for instance, the Roberta Initiative, the FIRST Lego League, the World Robot Olympiad and RoboCup Junior. Robotic competitions are valuable not only for school learners but also for university students, as the RoboCup initiative shows. Besides technical skills, the students get some project exposure and experience what it means to finish their tasks on time. But qualifying students for future high-tech areas should not only be for students from developed countries. In this article, we present our experiences with research and education in robotics within the RoboCup initiative, in Germany and South Africa; we report on our experiences with trying to get the RoboCup initiative in South Africa going. RoboCup has a huge support base of academic institutions in Germany; this is not the case in South Africa. We present our ‘north–south’ collaboration initiatives in RoboCup between Germany and South Africa and discuss some of the reasons why we think it is harder to run RoboCup in South Africa.
Ground or aerial robots equipped with advanced sensing technologies, such as three-dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state-of-the-art and off-the-shelf components and algorithms are suited for reconnaissance in current disaster-relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster-relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state-of-the-art and off-the-shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.
Rugged terrain robot designs are important for field robotics missions. A number of commercial platforms are available, however, at an impressive price. In this paper, we describe the hardware and software component of a low-cost wheeled rugged-terrain robot. The robot is based on an electric children quad bike and is modified to be driven by wire. In terms of climbing properties, operation time and payload it can compete with some of the commercially available platforms, but at a far lower price.
RGB-D sensors such as the Microsoft Kinect or the Asus Xtion are inexpensive 3D sensors. A depth image is computed by calculating the distortion of a known infrared light (IR) pattern which is projected into the scene. While these sensors are great devices they have some limitations. The distance they can measure is limited and they suffer from reflection problems on transparent, shiny, or very matte and absorbing objects. If more than one RGB-D camera is used the IR patterns interfere with each other. This results in a massive loss of depth information. In this paper, we present a simple and powerful method to overcome these problems. We propose a stereo RGB-D camera system which uses the pros of RGB-D cameras and combine them with the pros of stereo camera systems. The idea is to utilize the IR images of each two sensors as a stereo pair to generate a depth map. The IR patterns emitted by IR projectors are exploited here to enhance the dense stereo matching even if the observed objects or surfaces are texture-less or transparent. The resulting disparity map is then fused with the depth map offered by the RGB-D sensor to fill the regions and the holes that appear because of interference, or due to transparent or reflective objects. Our results show that the density of depth information is increased especially for transparent, shiny or matte objects.
A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. The novel RoboCup Logistics League Sponsored by Festo (LLSF) aims at such CPS logistic scenarios in an automation setting. A team of robots has to produce products from a number of semi-finished products which they have to machine during the game. Different production plans are possible and the robots need to recycle scrap byproducts. This way, the LLSF is a very interesting league offering a number of challenging research questions for planning, coordination, or communication in an application-driven scenario. In this paper, we outline the objectives of the LLSF and present steps for developing the league further towards a benchmark for logistics scenarios for CPS. As a major milestone we present the new automated referee system which helps in governing the game play as well as keeping track of the scored points in a very complex factory scenario.
Many biped robots deploy a form of gait that follows the zero moment point (ZMP) approach, that is, the robot is in a stable position at any point in time. This requires the robot to be fully actuated. While very stable, the draw-backs of this approach are a fairly slow gait and high energy consumption. An alternative approach is the so-called passive-dynamic walking, where the gait makes use of the inertia and dynamic stability of the robot. In this paper we describe our ongoing work of combining the principles of passive-dynamic walking on the fully-actuated biped robot Nao, which is also deployed for robotic soccer applications. We present a simple controller that allows the robot to stably rock sidewards, showing a closed limit-cycle. We discuss first results of superimposing a forward motion on the sidewards motion. Based on this we expect to endow the Nao with a fast, robust, and stable passive-dynamic walk on the fully-actuated Nao in the future.
South Africa in recent years is the establishment of a number of research hubs involved in AI activities ranging from mobile robotics and computational intelligence, to knowledge representation and reasoning, and human language technologies. In this survey we take the reader through a quick tour of the research being conducted at these hubs, and touch on an initiative to maintain and extend the current level of interest in AI research in the country.
In this paper we present CAESAR, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human–robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot CAESAR deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human–machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language READYLOG, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. READYLOG is a variant of the robot programming language Golog. We extended READYLOG to be able to cope with qualitative notions of space frequently used by humans, such as “near” and “far”. This facilitates human–robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use READYLOG to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in CAESAR and show the applicability of a system equipped with these AI techniques in domestic service robotics
Planwirtschaft
(2004)