The KBSG is looking for a new HiWi to assist in a research project on Optimizing the Performance of Robot Fleets in Production Logistics Scenarios Using SMT Solving
In this seminar we will study several modeling and reasoning techniques for knowledge and belief in dynamic systems. Knowledge is an important aspect of intelligent programs: while most of today's systems assume a closed world, i.e., everything they don't know to be true is assumed to be false, an intelligent system needs to consider possible that there are truths not known to the system. In a dynamic environment, i.e., an environment where one or multiple agents (inter)act, the system will usually have to acquire new knowledge through sensing. Potentially it may even revise its beliefs when it realizes some beliefs were wrong. In this seminar we will study various aspects of action, knowledge, and belief.
We plan to implement a peer review process for this seminar. That is, every student will read some other students' term paper and provide feedback in form of a written review. This shall not only deepen your understanding of the other topics, but it also introduces you to the academic review process.
Together with Bonding, the IMA/ZLW & IFU Institute Cluster, RWTH Aachen University and the Department for Electrical Engineering and Information Technology, Robotics Group, FH Aachen we will host the Robotino Bonding Hackathon. Students will compete in small teams to develop the behavior of a robot operating in a simplified desaster scenario to develop effective behavior strategies to recover and prioritize items from the arena.
The event is organized by the Carologistics RoboCup Team.
Mobile robots that are deployed, for instance, for domestic tasks, need to fulfill them even if they do not have complete information about the domain beforehand. Consider, for example, the task of fetching an object from a specific room. Consider further that the robot has a map of the environment and that it can plan a path to the living room. However, only when reaching the door of the living room, it will know whether or not the door is open. Moreover, it might not be known where in the living room the object is located such that the robot needs to make a plan how to find it. Next, a plan for grabbing the object in a safe way is required. This includes taking camera shots from different angles and finding an appropriate location to grab the object. Basically, the action control of the robot continuously needs to combine active perception and high-level planning with action execution. The robot has to deal with qualitative action and world descriptions as well as with uncertainty and quantitative data from sensors and actuators. The objective of this project is to develop methods for solving tasks like the above in an intelligent way by combining perception, high-level planning and action execution. An evaluation of the developed methods will be conducted on existing mobile robotic platforms.