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Decentralized Control of Intercity Electric Automated Buses via Time-Varying Objective Prioritization

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Decentralized Control of Intercity Electric Automated Buses ...

IEEE Conference on Decision and Control

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper considers electric automated buses traveling in inter-urban roads and following a given route including stops, that must be reached according to a given timetable. Some of these stops are provided with a charging infrastructure allowing to charge the bus batteries. The paper proposes a decentralized control scheme for determining the optimal speed profiles, the dwell and charging times of the buses, by taking into account the traffic conditions along the road through a suitable traffic flow prediction model. Two objectives are considered contemporarily: the minimization of the deviations from the timetable and the minimization of the energy lack at the end of the bus route. To attain both these conflicting objectives, a lexicographic approach is adopted to design the controller which considers that, depending on the system state, the priority of the two objectives can change. Accordingly, the proposed control scheme changes the objective prioritization in real time and switches between two different lexicographic-based optimal control solutions. Some tests are discussed in the paper to show the effectiveness of the proposed control scheme.

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A Software Framework to Encode the Psychological Dimensions of an Artificial Agent.

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A Software Framework to Encode the Psychological Dimensions ...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Alice Nardelli Carmine Recchiuto Antonio Sgorbissa Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via all’Opera Pia 13 Genoa Italy

Robotic personalities broaden the social dimension of an agent creating feelings of comfort in humans. In this work, we propose a taxonomy model to generate synthetic personalities based on the Big Five model. In particular, this paper describes a generalized framework for artificial personalities whose core is a Bidirectional Encoder Representations from Transformers (BERT) model capable of associating behaviors tailored to each personality trait. The generator is fully integrated within a modular software architecture capable of performing social interaction tasks, being at the same time task-and platform-independent. The proposed framework has been tested in a pilot experiment where human subjects were asked to interact with a humanoid robot displaying different personality traits. Results obtained by the statistical analysis of validated questionnaires show interesting insights about the capability of the framework of generating personalities that are clearly perceived by users, and whose personality dimensions are strongly distinguishable.

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A Framework for Neurosymbolic Robot Action Planning using Large Language Models

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arXiv 2023年

作者： Capitanelli, Alessio Mastrogiovanni, Fulvio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy

Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment in engineered robot architectures. However, techniques for symbolic task planning are difficult to scale in real-world, highly dynamic, human-robot collaboration scenarios because of the poor performance in planning domains where action effects may not be immediate, or when frequent re-planning is needed due to changed circumstances in the robot workspace. The validity of plans in the long term, plan length, and planning time could hinder the robot's efficiency and negatively affect the overall human-robot interaction's fluency. We present a framework, which we refer to as Teriyaki, specifically aimed at bridging the gap between symbolic task planning and machine learning approaches. The rationale is training Large Language Models (LLMs), namely GPT-3, into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL), and then leveraging its generative capabilities to overcome a number of limitations inherent to symbolic task planners. Potential benefits include (i) a better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, instead of super-linearly as in the case of symbolic task planners, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, and to make each action available for execution as soon as it is generated instead of waiting for the whole plan to be available, which in turn enables concurrent planning and execution. In the past year, significant efforts have been devoted by the research community to evaluate the overall cognitive capabilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to providing an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities in other metrics,

关键词： Human robot interaction

RICO-MR: An Open-Source Architecture for Robot Intent Communication through Mixed Reality

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arXiv 2023年

作者： Macciò, Simone Shaaban, Mohamad Carfì, Alessandro Zaccaria, Renato Mastrogiovanni, Fulvio The Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via Opera Pia 13 Genoa16145 Italy

This article presents an open-source architecture for conveying robots’ intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios. © 2023, CC BY.

关键词： Robots

A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks

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arXiv 2025年

作者： Karami, Hossein Thomas, Antony Mastrogiovanni, Fulvio Konecranes Finland Robotics Research Center IIIT Hyderabad Hyderabad500032 India Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All’Opera Pia 13 Genoa16145 Italy

In this paper, we present an approach for integrated task and motion planning based on an AND/OR graph network, which is used to represent task-level states and actions, and we leverage it to implement different classes of task and motion planning problems (TAMP). Several problems that fall under task and motion planning do not have a predetermined number of sub-tasks to achieve a goal. For example, while retrieving a target object from a cluttered workspace, in principle the number of object re-arrangements required to finally grasp it cannot be known ahead of time. To address this challenge, and in contrast to traditional planners, also those based on AND/OR graphs, we grow the AND/OR graph at run-time by progressively adding sub-graphs until grasping the target object becomes feasible, which yields a network of AND/OR graphs. The approach is extended to enable multi-robot task and motion planning, and (i) it allows us to perform task allocation while coordinating the activity of a given number of robots, and (ii) can handle multi-robot tasks involving an a priori unknown number of sub-tasks. The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink robotics. In particular, for the single-robot task and motion planning, we validated our approach in three different TAMP domains. Furthermore, we also use three different robots for simulation, namely, Baxter, Franka Emika Panda manipulators, and a PR2 robot. Experiments show that our approach can be readily scaled to scenarios with many objects and robots, and is capable of handling different classes of TAMP problems. © 2025, CC BY.

关键词： Multipurpose robots

Mixed Reality as Communication Medium for Human-Robot Collaboration

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Mixed Reality as Communication Medium for Human-Robot Collab...

IEEE International Conference on robotics and Automation (ICRA)

作者： Simone Macciò Alessandro Carfì Fulvio Mastrogiovanni Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy

ISBN: (数字)9781728196817

ISBN: (纸本)9781728196824

Humans engaged in collaborative activities are naturally able to convey their intentions to teammates through multi-modal communication, which is made up of explicit and implicit cues. Similarly, a more natural form of human-robot collaboration may be achieved by enabling robots to convey their intentions to human teammates via multiple communication channels. In this paper, we postulate that a better communication may take place should collaborative robots be able to anticipate their movements to human teammates in an intuitive way. In order to support such a claim, we propose a robot system's architecture through which robots can communicate planned motions to human teammates leveraging a Mixed Reality interface powered by modern head-mounted displays. Specifically, the robot's hologram, which is superimposed to the real robot in the human teammate's point of view, shows the robot's future movements, allowing the human to understand them in advance, and possibly react to them in an appropriate way. We conduct a preliminary user study to evaluate the effectiveness of the proposed anticipatory visualization during a complex collaborative task. The experimental results suggest that an improved and more natural collaboration can be achieved by employing this anticipatory communication mode.

关键词： Robot motion Visualization Collaboration Mixed reality Virtual reality Computer architecture Robot sensing systems

Machine Learning-Driven Dynamic Maps Supporting Wildfire Risk Management

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IFAC-PapersOnLine 2024年第2期58卷 67-72页

作者： Nicolò Perello Giorgio Meschi Andrea Trucchia Mirko D’Andrea Francesco Baghino Silvia degli Esposti Paolo Fiorucci Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All’Opera Pia 13 16145 Genoa Italy CIMA Research Foundation Via A. Magliotto 2 17100 Savona Italy

Recent decades have seen an increase in wildfires activity, posing risks to human settlements, and forcing exploration of new technologies for wildfire risk management. Utilizing Machine Learning in Time Series classification, this study produces decision support maps for Civil Protection system in Italy, which is responsible for coordinating national firefighting air fleet. Trained on past events data, the model gives daily indication on wildfire occurrence and aerial support requests for each administrative unit utilizing time series of Forest Fire Danger Rating indexes from RISICO model. Despite its recent implementation, it performed properly in 2023, showcasing model’s potential for decision support.

关键词： Wildfire risk management machine learning time series classification

Digital Twins for Human-Robot Collaboration: A Future Perspective

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arXiv 2023年

作者： Shaaban, Mohamad Carfì, Alessandro Mastrogiovanni, Fulvio Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All'opera Pia 13 Genova16145 Italy

As collaborative robot (Cobot) adoption in many sectors grows, so does the interest in integrating digital twins in human-robot collaboration (HRC). Virtual representations of physical systems (PT) and assets, known as digital twins, can revolutionize human-robot collaboration by enabling real-time simulation, monitoring, and control. In this article, we present a review of the state-of-the-art and our perspective on the future of digital twins (DT) in human-robot collaboration. We argue that DT will be crucial in increasing the efficiency and effectiveness of these systems by presenting compelling evidence and a concise vision of the future of DT in human-robot collaboration, as well as insights into the possible advantages and challenges associated with their integration. © 2023, CC BY-NC-ND.

关键词： Cyber Physical System

Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation

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arXiv 2023年

作者： Thomas, Antony Ferro, Giulio Mastrogiovanni, Fulvio Robba, Michela Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Via All’Opera Pia 13 Genoa16145 Italy

In this paper, we look into the minimum obstacle displacement (MOD) planning problem from a mobile robot motion planning perspective. This problem finds an optimal path to goal by displacing movable obstacles when no path exists due to collision with obstacles. However this problem is computationally expensive and grows exponentially in the size of number of movable obstacles. This work looks into approximate solutions that are computationally less intensive and differ from the optimal solution by a factor of the optimal cost. © 2023, CC BY.

关键词： Motion planning