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The RoboCup Logistics League (RCLL) is a robotics competition in a production logistics scenario in the context of a Smart Factory. In the competition, a team of three robots needs to assemble products to fulfill various orders that are requested online during the game. This year, the Carologistics team was able to win the competition with a new approach to multi-agent coordination as well as significant changes to the robot’s perception unit and a pragmatic network setup using the cellular network instead of WiFi. In this paper, we describe the major components of our approach with a focus on the changes compared to the last physical competition in 2019.
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.
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.
Autonomous agents require rich environment models for fulfilling their missions. High-definition maps are a well-established map format which allows for representing semantic information besides the usual geometric information of the environment. These are, for instance, road shapes, road markings, traffic signs or barriers. The geometric resolution of HD maps can be as precise as of centimetre level. In this paper, we report on our approach of using HD maps as a map representation for autonomous load-haul-dump vehicles in open-pit mining operations. As the mine undergoes constant change, we also need to constantly update the map. Therefore, we follow a lifelong mapping approach for updating the HD maps based on camera-based object detection and GPS data. We show our mapping algorithm based on the Lanelet 2 map format and show our integration with the navigation stack of the Robot Operating System. We present experimental results on our lifelong mapping approach from a real open-pit mine.
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.
Roboterfußball - Wissenschaft, die auch Spass macht / Ferrein, Alexander ; Lakemeyer, Gerhard
(2006)
In this extended abstract we describe the robot programming and planning language READYLOG, a GOLOG dialect which was developed to support the decision making of robots acting in dynamic real-time domains like robotic soccer. The formal framework of READYLOG, which is based on the situation calculus, features imperative control structures like loops and procedures, allows for decision-theoretic planning, and accounts for a continuously changing world. We developed high-level controllers in READYLOG for our soccer robots in RoboCup’s Middle-size league, but also for service robots and for autonomous agents in interactive computer games.
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.
Reasoning with Qualitative Positional Information for Domestic Domains in the Situation Calculus
(2011)
Planwirtschaft
(2004)
In order to allow an autonomous robot to perform non-trivial tasks like to explore a foreign planet the robot has to have deliberative capabilities like reasoning or planning. Logic-based approaches like the programming and planing language Golog and it successors has been successfully used for such decision-making problems. A drawback of this particular programing language is that their interpreter usually are written in Prolog and run on a Prolog back-end. Such back-ends are usually not available or feasible on resource-limited robot systems. In this paper we present our ideas and first results of a re-implementation of the interpreter based on the Lua scripting language which is available on a wide range of systems including small embedded systems.
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.
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.