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

A Survey of Knowledge Representation in Service Robotics

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action Representations for Autonomous Robots - 22 Page

Modelling, analysis and execution of robotic tasks using petri nets

Abstract—This paper introduces Petri net based models of robotic tasks, which can be used to analyse and synthesise task plans, taking into account a Petri net model that abstracts the relevant features from the robot environment as well. Logical analysis concerning deadlocks and resource conservation can be performed over the ordinary version of the model. A task plan modeled by a Petri net can be extracted from the generalised stochastic version of the model, representing the optimal plan given a probabilistic measure of uncertainty associated to the effects of its composing actions. The Petri net representing the model is suitable for being ran directly within the code, as well as for plan monitoring during execution time. Simulation results illustrating the methodology are presented for a robotic soccer scenario. I