PLATINUm: A New Framework for Planning and Acting

Umbrico, Alessandro; Cesta, Amedeo; Mayer, Marta Cialdea; Orlandini, Andrea

doi:10.1007/978-3-319-70169-1_37

Cited by 22 publications

(12 citation statements)

References 14 publications

(26 reference statements)

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“…Task planning capabilities developed within SHAREWORK rely on the timelinebased formalism [26] and the PLATINUm software framework [27][28][29]. This planning formalism integrates reasoning about causal and temporal aspects of a planning problem and has been successfully applied to several concrete scenarios [30][31][32].…”

Section: User-aware Collaborationmentioning

confidence: 99%

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

et al. 2022

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Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The coexistence of human and autonomous robotic agents raises several methodological and technological challenges for the design of effective, safe, and reliable control paradigms. This work proposes the integration of novel technologies from Artificial Intelligence, Control and Augmented Reality to enhance the flexibility and adaptability of collaborative systems. We present the basis to advance the classical human-aware control paradigm in favor of a user-aware control paradigm and thus personalize and adapt the synthesis and execution of collaborative processes following a user-centric approach. We leverage a manufacturing case study to show a possible deployment of the proposed framework in a real-world industrial scenario.

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Section: User-aware Collaborationmentioning

confidence: 99%

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

et al. 2022

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“…As a concrete planning and scheduling tool, we consider a timeline-based software framework, called PLATINUm [12]. PLATINUm complies with the formal characterization of the timeline-based approach proposed in [20] which takes into account also temporal uncertainty.…”

Section: Automated Planning and Scheduling For Hrcmentioning

confidence: 99%

“…This paper proposes a novel software tool, called "Tool fostEriNg Ai plaNning in roboTics" (TENANT), addressing the needs of Domain Experts to set goals, defining tasks and set operational constraints notwithstanding the intrinsic complexity required at planning and robotics level. TEN-ANT is a general purpose tool that can be deployed for addressing multiple applications/domains and can be easily integrated with other knowledge engineering tools such as, e.g., ROS-TiPlEx [7] and Planning and Scheduling software framework, e.g., PLATINUm [12]. TENANT specifically focuses on Human-Robot Collaboration and allows domain experts to specify collaborative models and thus describe tasks needed to achieve desired (productive) goals.…”

Section: Introductionmentioning

confidence: 99%

Simplifying the A.I. Planning modeling for Human-Robot Collaboration

Elisa

Cesta

Umbrico

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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For an effective deployment in manufacturing, Collaborative Robots should be capable of adapting their behavior to the state of the environment and to keep the user safe and engaged during the interaction. Artificial Intelligence (AI) enables robots to autonomously operate understanding the environment, planning their tasks and acting to achieve some given goals. However, the effective deployment of AI technologies in real industrial environments is not straightforward. There is a need for engineering tools facilitating communication and interaction between AI engineers and Domain experts. This paper proposes a novel software tool, called TENANT (Tool fostEriNg Ai plaNning in roboTics) whose aim is to facilitate the use of AI planning technologies by providing domain experts like e.g., production engineers, with a graphical software framework to synthesize AI planning models abstracting from syntactic features of the underlying planning formalism.

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“…The Task Planner relies on Timeline-based Planning and Scheduling (P&S) [10], a temporal planning paradigm that takes inspiration from the classical Control Theory and has been successfully applied in many real-world applications (e.g., [11] The Task Planner is implemented through PLATINUm [12], a timeline-based software framework for P&S able to deal with temporal uncertainty and already successfully deployed in real-world manufacturing scenarios [2]. A planning synthesis module (Task Plan Synthesis in Fig.…”

Section: A Task Plannermentioning

confidence: 99%

A Layered Control Approach to Human-Aware Task and Motion Planning for Human-Robot Collaboration

Faroni

Beschi

Ghidini

et al. 2020

2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

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Combining task and motion planning efficiently in human-robot collaboration (HRC) entails several challenges because of the uncertainty conveyed by the human behavior. Tasks plan execution should be continuously monitored and updated based on the actual behavior of the human and the robot to maintain productivity and safety. We propose control-based approach based on two layers, i.e., task planning and action planning. Each layer reasons at a different level of abstraction: task planning considers high-level operations without taking into account their motion properties; action planning optimizes the execution of high-level operations based on current human state and geometric reasoning. The result is a hierarchical framework where the bottom layer gives feedback to top layer about the feasibility of each task, and the top layer uses this feedback to (re)optimize the process plan. The method is applied to an industrial case study in which a robot and a human worker cooperate to assemble a mosaic.

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PLATINUm: A New Framework for Planning and Acting

Cited by 22 publications

References 14 publications

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations

Simplifying the A.I. Planning modeling for Human-Robot Collaboration

A Layered Control Approach to Human-Aware Task and Motion Planning for Human-Robot Collaboration

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