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Daniele Calisi

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4 papers

2 author rows

ICAPS Conference 2023 Conference Paper

Planning for Automated Testing of Implicit Constraints in Behavior Trees

Uwe Köckemann
Daniele Calisi
Guglielmo Gemignani
Jennifer Renoux
Alessandro Saffiotti

Behavior Trees (BTs) are a formalism increasingly used to control the execution of robotic systems. The strength of BTs resides in their compact, hierarchical and transparent representation. However, when used in practical applications transparency is often hindered by the introduction of implicit run-time relations between nodes, e. g. , because of data dependencies or hardware-related ordering constraints. Manually verifying the correctness of a BT with respect to these hidden relations is a tedious and error-prone task. This paper presents a modular planning-based approach for automatically testing BTs offline at design time, to identify possible executions that may violate given data and ordering constraints and to exhibit traces of these executions to help debugging. Our approach supports both basic and advanced BT node types, e. g. , supporting parallel behaviors, and can be extended with other node types as needed. We evaluate our approach on BTs used in a commercially deployed robotics system and on a large set of randomly generated trees showing that our approach scales to realistic sizes of more than 3000 nodes.

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AAMAS Conference 2011 Conference Paper

Experimental Evaluation of Teamwork in Many-Robot Systems

Andrea D'Agostini
Daniele Calisi
Alberto Leo
Francesco Fedi
Luca Iocchi
Daniele Nardi

This paper describes a Multi-robot System, its features and a set of experiments that show its performance under different conditions (e. g. , network and sensor unrealiability, number of robots, etc). The system has been fully deployed in a simulated environment; in addition, experiments with a hybrid team of cooperating simulated and real robots will be presented.

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ICRA Conference 2008 Conference Paper

A Bayesian framework for optimal motion planning with uncertainty

Andrea Censi
Daniele Calisi
Alessandro De Luca 0001
Giuseppe Oriolo

Modeling robot motion planning with uncertainty in a Bayesian framework leads to a computationally intractable stochastic control problem. We seek hypotheses that can justify a separate implementation of control, localization and planning. In the end, we reduce the stochastic control problem to path- planning in the extended space of poses x covariances; the transitions between states are modeled through the use of the Fisher information matrix. In this framework, we consider two problems: minimizing the execution time, and minimizing the final covariance, with an upper bound on the execution time. Two correct and complete algorithms are presented. The first is the direct extension of classical graph-search algorithms in the extended space. The second one is a back-projection algorithm: uncertainty constraints are propagated backward from the goal towards the start state.

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IROS Conference 2008 Conference Paper

OpenRDK: A modular framework for robotic software development

Daniele Calisi
Andrea Censi
Luca Iocchi
Daniele Nardi

Intense efforts to define a common structure in robotic applications, both from a conceptual and from an implementation point of view, have been carried out in the last years and several frameworks have been realized for helping in developing robotic applications. However, due to the diversity of these applications, as well as of the research groups involved, a common framework is still far from being accepted. In this paper we focus on modularity and re-usability, as major features for robotic applications. We thus characterize existing frameworks for robot software development through the choices made on concurrent execution of modules and information sharing among them and we present OpenRDK, a modular framework focused on rapid development of distributed robotic systems. OpenRDK has been designed and developed with many years of experience following userspsila advice and has been successfully used for the development of many diverse applications with different kinds of robots. After such an extensive test, OpenRDK is now an open source project.

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