Sebastian Reuter

RoboCup Logistics League Sponsored by Festo: A Competitive Factory Automation Testbed

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 a 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.

Towards Benchmarking Cyber-Physical Systems in Factory Automation Scenarios

A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. In future factory automation scenarios, mobile robots will play an important role to help customizing the production process, for instance, by transporting semi-products and raw materials to the machines. Therefore it will be important to compare the performance of mobile robots in such future logistics tasks. In this paper we sketch how the novel RoboCup Logistics League with its automated referee and overhead tracking system can be developed into a standard benchmark for logistics application in factory automation scenarios.

Evaluation of the RoboCup Logistics League and Derived Criteria for Future Competitions

In the RoboCup Logistics League RCLL, games are governed by a semi-autonomous referee box. It also records tremendous amounts of data about state changes of the game or communication with the robots. In this paper, we analyze the data of the 2014 competition by means of Key Performance Indicators KPI. KPIs are used in industrial environments to evaluate the performance of production systems. Applying adapted KPIs to the RCLL provides interesting insights about the strategies of the robot teams. When aiming for more realistic industrial properties with a 24/7 production, where teams perform shifts without intermediate environment reset, KPIs could be a means to score the game. This could be tried first in a simulation sub-league.

Decisive Factors for the Success of the Carologistics RoboCup Team in the RoboCup Logistics League 2014

The RoboCup Logistics League is one of the youngest application- and industry-oriented leagues. Even so, the complexity and level of difficulty has increased over the years. We describe decisive technical and organizational aspects of our hardware and software systems and (human) team structure that made winning the RoboCup and German Open competitions possible in 2014.

Cyber-Physical System Intelligence

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.

Improvements for a Robust Production in the RoboCup Logistics League 2016.

In 2016, the RoboCup Logistics League (RCLL) scenario has received only minor changes. The Carologistics team used this time to stabilize and improve several of its system’s components to make overall production more robust and support a more elaborated domain modeling. This has been made possible especially through our simulation environment. We describe the major aspects of our efforts that led to winning the competition for the third time in a row.

The Carologistics Approach to Cope with the Increased Complexity and New Challenges of the RoboCup Logistics League 2015

The RoboCup Logistics League RCLL has seen major rule changes increasing the complexity, e.g. by raising the number of product variants from 3 to almost 250, and introducing new challenges like the handling of physical processing machines. We describe various aspects of our system that allowed to improve the performance in 2015 and our efforts to advance the league as a whole.

Design and implementation of a vehicle dynamics control system by means of torque vectoring for an autonomous vehicle

This paper describes to which extent the operational envelope of scaled vehicles can be extended by integrating a vehicle dynamics control system using torque vectoring. A detailed description for the construction of a mechatronic basis for an autonomous model car in a 1:10th scale is given. The design of the vehicle only uses standardized components and allows the most possible flexibility of controlling the vehicles dynamics behavior by applying individual driving moments to the wheels.

Benchmarking of Cyber-Physical Systems in Industrial Robotics

Abstract 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.

An adaptive position optimization approach for an actuated localization network in solid glacier environments

This paper proposes a novel approach for an agent-based, autonomous, self-configuring pinger network for an extraterrestrial glacier environment. The task of this network is to span up a reference system for localization of an autonomous melting probe along a preplanned trajectory in glacier ice. Due to the fact that this network is developed for an extraterrestrial mission, unpredictable circumstances can occur that lead to malfunctions of components. Malfunctioning components cannot be repaired by humans and therefore the the agent-system is decentrally designed with redundancies to ensure robust operation of the network and to compensate for malfunctions of individual agents. The individual agents of the network use ultrasonic signals to communicate and determine mutual positions by trilateration of acoustic signal propagation times in ice. We present a knowledge-based agent architecture and its optimization component that aims for high localization coverage and a low localization error of the autonomous melting probe along its planned trajectory.