Enhancing production efficiency, ensuring consistent quality, and significantly reducing manufacturing costs are core benefits of deploying robotic solutions to manufacturing tasks. Leading to automation of assembly tasks being a focus of robotics research since many years, and many solutions have already been successfully deployed into the industry. However, applying those approaches in a human-robot collaboration (HRC) scenario is challenging. Humans introduce an additional uncertainty factor that prohibits pure offline planning and requires plan adaptation during execution. Intelligent planning approaches are therefore needed that consider human intervention from the start and enable further progress toward flexible HRC assembly systems. This paper presents a novel approach to flexible robotic task planning by utilizing assembly affordances, which are perceived opportunities for actions that a component offers in terms of assembly. Our approach integrates affordance-based reasoning within a semantic planning framework to allow online refinement of assembly plans to be used in fully automated scenarios as well as HRC assembly scenarios. The paper presents the developed concept and framework and evaluates it based on the success rate on assemblies of different complexities.

While individual robots are becoming increasingly capable, the complexity of expected missions increases exponentially in comparison. To cope with this complexity, heterogeneous teams of robots have become a significant research interest in recent years. Making effective use of the robots and their unique skills in a team is challenging. Dynamic runtime conditions often make static task allocations infeasible, requiring a dynamic, capability-aware allocation of tasks to team members. To this end, we propose and implement a system that allows a user to specify missions using Behavior Trees (BTs), which can then, at runtime, be dynamically allocated to the current robot team. The system allows to statically model an individual robot’s capabilities within our ros_bt_py BT framework. It offers a runtime auction system to dynamically allocate tasks to the most capable robot in the current team. The system leverages utility values and pre-conditions to ensure that the allocation improves the overall mission execution quality while preventing faulty assignments. To evaluate the system, we simulated a find-and-decontaminate mission with a team of three heterogeneous robots and analyzed the utilization and overall mission times as metrics. Our results show that our system can improve the overall effectiveness of a team while allowing for intuitive mission specification and flexibility in the team composition.

Field and service robots that do not understand the hazards in their environment limit their potential by acting overly careful or navigating into potentially dangerous areas. We present an extended modular mapping framework which allows to model different types of hazards from a multitude of inputs. The proposed approach is generalized for storage of arbitrary data, therefore not limiting the usage to one use case. Furthermore the framework allows the fusion of risks to calculate the overall risk for an individual robot from its surroundings. The system was tested with LAURON V, a hexapod designed for walking over rough and hazardous terrain.

In this work, the ReApp Engineering Workbench and its underlying semantically enriched app models are presented. The usage of a model, which describes the apps functionality, interfaces and other attributes, allows the utilization of engineering tools for code generation and automated testing. Further, it ensures the compatibility of the generated interfaces, which in turn enhances the reusability of the developed apps in larger applications.

Multi-legged walking robots often make use of sophisticated control architectures to play their strengths in rough and unknown environments. The adaptability of these robots is an essential skill to achieve the maneuverability and autonomy needed in their application fields. In this work we present a reactive control approach for the hexapod LAURONV, which enables it to overcome large obstacles and steep slopes without any knowledge about the environment. A key to this success can also be seen in the increased kinematic adaptability due to the fourth rotational joint in the bio-inspired leg kinematics. An extended experimental evaluation shows that the reactive posture behaviors are able to create an effective and efficient locomotion in challenging environments.