The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper we describe our work on a Webots simulation environment for a Robotino platform extended by LIght Detection And Ranging (LIDAR) sensors. A ROS 2 integration and a pre-configured setup for localization and navigation using existing ROS packages from the Nav2 suite is provided. We validate our setup by comparing simulations with real-world experiments conducted by three Robotinos in a logistics environment in our lab. Additionally, we tested the setup using a ROS 2 hardware driver for the Robotino developed by team GRIPS of the RoboCup Logistics League. The results demonstrate the feasibility of using ROS 2 and Nav2 for navigation tasks on the Robotino platform showing great consistency between simulation and real-world performance.

In this paper, we are concerned with making the execution of abstract action plans for robotic agents more robust. To this end, we propose to model the internals of a robot system and its ties to the actions that the robot can perform. Based on these models, we propose an online transformation of an abstract plan into executable actions conforming with system specifics. With our framework, we aim to achieve two goals. First, modeling the system internals is beneficial in its own right in order to achieve long term autonomy, system transparency, and comprehensibility. Second, separating the system details from determining the course of action on an abstract level leverages the use of planning for actual robotic systems.

Facing the fourth industrial revolution, autonomous mobile robots are expected to play an important role in the production processes of the future. The new Logistics League Sponsored by Festo (LLSF) under the RoboCup umbrella focuses on this aspect of robotics to provide a benchmark testbed on a common robot platform. We describe certain aspects of the integrated robot system of our Carologistics RoboCup team, in particular our reasoning system for the supply chain problem of the LLSF. We approach the problem by deploying the CLIPS rules engine for product planning and dealing with the incomplete knowledge that exists in the domain and show that it is suitable for computationally limited platforms.

In this paper we outline the development of \Caesar{}, a domestic service robot with which we participated in the robot competition RoboCup@Home for many years. We sketch the system components, in particular the parts relevant to the high-level reasoning system, that make CAESAR an intelligent robot. We report on the development and discuss the lessons we learnt over the years designing, developing and maintaining an intelligent service robot. From our perspective of having participated in RoboCup@Home for a long time, we answer the core questions of the workshop about platforms, challenges and the evaluation of integrative research.

One of the consequences of the growth in AI research in 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.

According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country. The South African tertiary education sector is quite small for a country with a population of around 50 million, with 11 "traditional" universities, 6 technical universities, and 6 comprehensive universities. The latter university types focus on more technical or vocational education. The public sector also funds 16 research institutions. In spite of these obstacles, South African universities participate in world-class research activities in many fields and range among the best on the African continent.

Among many approaches to address the high-level decision making problem for autonomous robots and agents, the robot programming and plan language Golog follows a logic-based deliberative approach, and its successors were successfully deployed in a number of robotics applications over the past ten years. Usually, Golog interpreter are implemented in Prolog, which is not available for our target platform, the bi-ped robot platform Nao. In this paper we sketch our novel prototype implementation of a Golog interpreter in the scripting language Lua. With the example of the elevator domain we discuss how the basic action theory is specified and how we implemented fluent regression or backtracking in Lua. One possible advantage of the availability of a Non-Prolog implementation of Golog could be that Golog becomes available on a larger number of platforms, and also becomes more attractive for roboticists outside the Cognitive Robotics community.