An execution engine for aerial robot mission plans

The goal of the work presented in this paper is to develop a practical solution for mission plan execution to simplify the way in which operators configure the missions of robots. This work has been done to promote a more extensive use of the software framework for aerial robotics Aerostack. We have designed a computer system called execution engine that includes technical solutions from general robotics and artificial intelligence. The system follows a behavior-based approach and a symbolic representation of beliefs. The execution engine has been designed to be part of Aerostack but it can also work independently, so that it can be reused for building other type of robot architectures. This paper has been written as a specification and software design to be used as a guide for software implementation of the execution engine

A Survey on Domain-Specific Modeling and Languages in Robotics

Nordmann A, Hochgeschwender N, Wigand DL, Wrede S. A Survey on Domain-Specific Modeling and Languages in Robotics. Journal of Software Engineering in Robotics. 2016;7(1):75-99

Petri Net Plans A framework for collaboration and coordination in multi-robot systems

Programming the behavior of multi-robot systems is a challenging task which has a key role in developing effective systems in many application domains. In this paper, we present Petri Net Plans (PNPs), a language based on Petri Nets (PNs), which allows for intuitive and effective robot and multi-robot behavior design. PNPs are very expressive and support a rich set of features that are critical to develop robotic applications, including sensing, interrupts and concurrency. As a central feature, PNPs allow for a formal analysis of plans based on standard PN tools. Moreover, PNPs are suitable for modeling multi-robot systems and the developed behaviors can be executed in a distributed setting, while preserving the properties of the modeled system. PNPs have been deployed in several robotic platforms in different application domains. In this paper, we report three case studies, which address complex single robot plans, coordination and collaboration

Learning and Execution of Object Manipulation Tasks on Humanoid Robots

Equipping robots with complex capabilities still requires a great amount of effort. In this work, a novel approach is proposed to understand, to represent and to execute object manipulation tasks learned from observation by combining methods of data analysis, graphical modeling and artificial intelligence. Employing this approach enables robots to reason about how to solve tasks in dynamic environments and to adapt to unseen situations