Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

Robots acting in real-world environments may interact with humans at different levels of abstraction (e.g., process, task, physical), entailing different control and coordination challenges. When acting in social situations, robots should be able to pursue (joint) goals by behaving according to the context as well as the skills/features of involved humans. Although reliable and effective, standard control techniques may limit the adaptability of robots. Novel control technologies based on Artificial Intelligence can endow robots with the cognitive capabilities needed to achieve a higher level of autonomy in terms of flexibility, reliability, and awareness. In this context, this paper introduces a goal-oriented acting framework based on timeline-based planning and execution. The framework is evaluated on a realistic Human-Robot Collaboration manufacturing scenario. Results show the capability of dealing with the uncontrollable dynamics of humans achieving effective and reliable collaborations.

Abstract Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from giving advices to promote an healthy lifestyle to psychological monitoring. Among possible users, frail older adults deserve a special focus as they present a rich variability in terms of both alternative possible assistive scenarios (e.g., hospital or domestic environments) and caring needs that could change over time according to their health conditions. In this perspective, robot behaviors should be customized according to properly designed user models. One of the long-term research goals for SAR is the realization of robots capable of, on the one hand, personalizing assistance according to different health-related conditions/states of users and, on the other, adapting behaviors according to heterogeneous contexts as well as changing/evolving needs of users. This work proposes a solution based on a user model grounded on the international classification of functioning, disability and health (ICF) and a novel control architecture inspired by the dual-process theory. The proposed approach is general and can be deployed in many different scenarios. In this paper, we focus on a social robot in charge of the synthesis of personalized training sessions for the cognitive stimulation of older adults, customizing the adaptive verbal behavior according to the characteristics of the users and to their dynamic reactions when interacting. Evaluations with a restricted number of users show good usability of the system, a general positive attitude of users and the ability of the system to capture users personality so as to adapt the content accordingly during the verbal interaction.

Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts of human workers, robots, and intelligent entities. This is particularly crucial in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and intelligent autonomous robots is necessary. The work proposes the integration of novel Artificial Intelligence technologies to enhance the flexibility and adaptability of collaborative robots. The integrated functionalities allow a collaborative robot to autonomously recognize the tasks a human worker performs, and accordingly adapt its behavior. The approach is deployed on a real HRC scenario showing the functioning of the developed cognitive capabilities and the increased flexibility of resulting collaborations.

Cyber-Physical Systems constitute one of the core concepts in Industry 4.0 aiming at realizing production systems that combine the efforts from human workers, robots and intelligent entities. This is particularly true in Human-Robot Collaboration manufacturing where a tight peer-to-peer interaction between humans and (intelligent) autonomous robots is necessary. Such production systems need a holistic integration along different levels of abstraction and coordination for deploying effective and safe control solutions. We propose the use of novel Artificial Intelligence technologies to enhance flexibility and adaptability of these collaborative systems. Our aim is to advance the classical human-aware paradigm that considers the worker as an anonymous acting entity, in favour of a user-aware paradigm, that considers a worker as profiled user characterized with a number of specific features influencing the “shape” of the collaboration.

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.

This paper discusses the issue of efficient resolution of timeline-based planning problems. In particular, taking inspiration from the more classical heuristics for the resolution of STRIPS-like problems, it proposes a new heuristic strategy which, while maintaining the variables lifted, allows more accurate decisions. The concepts presented in this work pave the way for a new type of heuristics which, at present, allow this kind of solvers a significant performance improvement.

A key aspect of robotic assistants is their ability to contextualize their behavior according to different needs of assistive scenarios. This work presents an ontology-based knowledge representation and reasoning approach supporting the synthesis of personalized behavior of robotic assistants. It introduces an ontological model of health state and functioning of persons based on the International Classification of Functioning, Disability and Health. Moreover, it borrows the concepts of affordance and function from the literature of robotics and manufacturing and adapts them to robotic (physical and cognitive) assistance domain. Knowledge reasoning mechanisms are developed on top of the resulting ontological model to reason about stimulation capabilities of a robot and health state of a person in order to identify action opportunities and achieve personalized assistance. Experimental tests assess the performance of the proposed approach and its capability of dealing with different profiles and stimuli.

Socially assistive robots should provide users with personalized assistance within a wide range of scenarios such as hospitals, home or social settings and private houses. Different people may have different needs both at the cognitive/physical support level and in relation to the preferences of interaction. Consequently the typology of tasks and the way the assistance is delivered can change according to the person with whom the robot is interacting. The authors' long-term research goal is the realization of an advanced cognitive system able to support multiple assistive scenarios with adaptations over time. We here show how the integration of model-based and model-free AI technologies can contextualize robot assistive behaviors and dynamically decide what to do (assistive plan) and how to do it (assistive plan execution), according to the different features and needs of assisted persons. Although the approach is general, the paper specifically focuses on the synthesis of personalized therapies for (cognitive) stimulation of users.

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.

This paper presents a novel comprehensive framework called ROS-TiPlEx (Timeline-based Planning and Execution with ROS) to provide a shared environment in which experts in robotics and planning can easily interact to, respectively, encode information about low-level robot control and define task planning and execution models. ROS-TiPlEx aims at facilitating the interaction between both kind of experts, thus, enhancing and possibly speeding up the process of an integrated control design. ROS-TiPlEx is the first tool addressing the connection of ROS and timeline-based planning.

The deployment of assistive robots in everyday life scenarios and their capability of providing an effective and useful support for independent living is an open and challenging research problem. The development of suitable robot control systems requires effective solutions for addressing issues concerning performance, reliability, flexibility and proactivity. In this work, we propose an AI-based cognitive architecture aiming at integrating knowledge representation with automated planning and execution techniques in order to endow assistive robots with proactivity and self-configuration capabilities.

The collaboration between humans and robots is a current technological trend that faces various challenges, among these the seamless integration of the respective working capabilities. Industrial robots have demonstrated their capacity to meet the needs of many applications, offering accuracy and efficiency, while humans have both experience and the capability to elaborate over such experience that are absolutely not replaceable at any time. Clearly a symbiotic integration of humans and robots in working scenarios opens to new problems: for an effective collaboration an intelligent coordination is required. This paper presents an interactive environment for facilitating the collaboration between humans and a robot in performing shared tasks in industrial environments. In particular we introduce a tool based on AI planning technology to help the smooth intertwining of activities of the two actors in the work environment. The paper presents a case study from a real world environment, describes a comprehensive architectural approach to the problem of coordinated interaction, and then presents details on the current status of the tool.

In this paper, we tackle the Energy-Flexible Flow Shop Scheduling (EnFFS) problem, a multi-objective optimization problem focused on the minimization of both the overall completion time C and the global energy consumption E of the solutions. The tackled problem is an extension of the Flexible Flow-Shop Scheduling problem where each activity in a job has a set of possible execution modes with different trade-off between energy consumed and processing time. Moreover, global energy consumption E may also depend on the possibility to switch-off the machines during the idle periods. The goal of this work is to widen the knowledge about performance capabilities, in particular the ability of efficiently finding high quality approximations of the solution Pareto front. To this aim, we explore the development of innovative meta-heuristic algorithms for solving the proposed multi-objective scheduling problem. In particular, we consider stochastic local search (SLS) algorithms, introducing a Multi-Objective Large Neighbourhood Search (MO-LNS) framework in line with the large neighbourhood search approaches proposed in literature, and present some preliminary results obtained against a EnFFS benchmark recently proposed in the literature, showing some initial but appreciable improvements. Moreover, four new instances are presented and experimented upon, which will be hopefully used by the community as a novel benchmark on which to test new multi-objective optimisation research contributions.

This paper presents the mid-term outcome of the Generic Evolutionary Control Knowledge-based mOdule (Gecko) research project, i.e., a layered architecture to implement a cooperative model-based control agent for a Reconfigurable Transportation System (RTSs). A manufacturing plant is here conceived as multiple independent modules to implement alternative inbound logistic systems' configurations. To support this capability of the mechatronic hardware, an integrated solution is proposed using a knowledge-based approach to support a timeline-based planning and control module responsible for managing both the node regular activities and reconfiguration activities. A cooperation layer dedicated to multi-module coordination completes the overall architecture.

Early detection and adaptive support to changing individual needs related to ageing is an important challenge in today's society. In this paper we present a system called GiraffPlus that aims at addressing such a challenge and is developed in an on-going European project. The system consists of a network of home sensors that can be automatically configured to collect data for a range of monitoring services; a semi-autonomous telepresence robot; a sophisticated context recognition system that can give high-level and long term interpretations of the collected data and respond to certain events; and personalized services delivered through adaptive user interfaces for primary users. The system performs a range of services including data collection and analysis of long term trends in behaviors and physiological parameters (e.g. relating to sleep or daily activity); warnings, alarms and reminders; and social interaction through the telepresence robot. The latter is based on the Giraff telepresence robot, which is already in place in a number of homes. A distinctive aspect of the project is that the GiraffPlus system will be installed and evaluated in at least 15 homes of elderly people. This paper provides a general overview of the GiraffPlus system and its evaluation.

Safety critical planning and execution is a crucial issue in autonomous systems. This paper proposes a methodology for controller synthesis suitable for timeline-based planning and demonstrates its effectiveness in a space domain where robustness of execution is a crucial property. The proposed approach uses Timed Game Automata (TGA) for formal modeling and the UPPAAL-TIGA model checker for controllers synthesis. An experimental evaluation is performed using a real-world control system.

Highly automated production systems are conceived to efficiently handle evolving production requirements. This concerns any level of the system from the configuration and control to the management of production. The proposed work deals with the development of an innovative platform jointly managing the production scheduling level and the automation level. The major advantage coming from the platform is the capacity of generating scheduling plans which are executed at automation level and concurrently monitored over time so that any production anomaly or system misbehavior can be dynamically interpreted and adapted by regenerating online a new schedule. The paper describes the current release of our closed loop architecture that integrated both control and automation parts.

Telepresence robots can become a beneficial tool in home care assistance and rehabilitation services by helping elderly people to remain in their homes longer. They can represent an additional means to assist older adults and facilitate social interaction by creating a support network through which nursing staff and family members can collaborate. This article describes a feasibility study relatively to the use of such robots in the interaction with elderly people affected by Mild Cognitive Impairment (MCI). The paper aims at assessing the psychophysiological response of such users to the presence of the robotic platform in order to use it as an indication of the level of tolerance toward the platform. To this purpose, we have involved 9 healthy and 8 MCI older adults in the participation of an experimental study where they have been asked to perform repeated interactions with and without the telepresence robot. We based our analysis on a combination of psychological tests to assess anxiety, positive/negative effects of the interaction with the robot, and we performed physiological measurements (heart rate and heart rate variability) to obtain an objective measure of the actual psychological state. Results seem to suggest that the robot presence is satisfactorily tolerated by MCI and that it does not cause adverse effects in term of cardiovascular response, thus encouraging further investigation on telepresence robots for rehabilitation and care experimental studies.

No abstract available.

Highly reconfigurable and agile production systems are selected to operate in production contexts often characterized by changes of the production requirements or changes of the part family demand. The operational level for such system architecture is expected to manage the short term production planning while guaranteeing the automation layer enables physical devices to exploit logic control tasks within the specific time buckets. The proposed work outlines an integrated approach supporting the operational level for RMSs in which the scheduling of production jobs and the scheduling of corresponding automation tasks are dynamically coupled. Connections with consolidated constraint-based representation and solving techniques are also discussed. The integrated scheduling approach has been validated with reference to a Finishing Robotic Cell (FRC) operating in a pilot assembly line.

This article elaborates around a recent effort to build a planning system that helps human mission planning in a space mission. Specifically, the article describes the steps that brought us to develop MrSPOCK, the MARS EXPRESS Science Plan Opportunities Coordination Kit, a tool for supporting long‐term planning in the MARS EXPRESS mission of the European Space Agency. In showing our effort for creating MrSPOCK, we will underscore the key ingredients for developing complete applications and how they are connected to a stable line of research on planning and scheduling with timelines.

This article describes aspects of a fully implemented artificial intelligence (AI) system that integrates multiple intelligent components to actively assist an elderly person at home. Specifically, we describe how constraint‐based scheduling technology is used to actively monitor a pattern of activities executed by the person and how detected temporal constraint violations are used to trigger meaningful and contextualized proactive interactions. This article also presents a psychological evaluation of the system focusing on elderly people's attitudes, in which system acceptability, perceived utility, interaction modality, and emotional response are considered.

Timeline-based planning techniques have demonstrated wide application possibilities in heterogeneous real world domains. For a wider diffusion of this technology, a more thorough investigation of the connections with formal methods is needed. This pa

Timeline-based planners have been shown quite successful in addressing real world problems. Nevertheless they are considered as a niche technology in AI P&S research as an application synthesis with such techniques is still considered a sort of “black art”. Authors are currently developing a knowledge engineering tool around a timeline-based problem solving environment; in this framework we aim at integrating verification and validation methods. This work presents a verification process suitable for a timeline-based planner. It shows how a problem of flexible temporal plan verification can be cast as model-checking on timed game automata. Additionally it provides formal properties and checks the effectiveness of the proposed approach with a detailed experimental analysis.

The main result of the paper is the reduction of the RCPSP/max problem to a Disjunctive Temporal Problem that allows customization of specific properties within a backtracking search procedure for makespan optimization. In addition, a branching strategy is proposed able to deduce new constraints which explicitly represent infeasible or useless search paths. A new variable ordering heuristic (called clustering) is also used which provides a further boosting to the algorithm's effectiveness.

This paper describes RAXEM, an AI-based system developed to support human mission planners in the daily task to plan uplink commands for an interplanetary spacecraft. The intelligent environment of RAXEM has been designed to support the users in analyzing the problem and taking planning decisions as a result of an interactive process. The system combines different ingredients like integrating flexible automated algorithms, promoting user active participation during problem solving, and guaranteeing continuity of work practice. The paper touches upon all these aspects and comments on how a key factor for success has been the integration of intelligent technology to continuously support mission plan management.

The aim of this article is to bring forth the issue of integrating the services provided by intelligent artifacts in Ambient Intelligence applications. Specifically, we propose a Distributed Constraint Optimization procedure for achieving a functional integration of intelligent artifacts in a smart home. To this end, we employAdopt‐N, a state‐of‐the‐art algorithm for solving Distributed Constraint Optimization Problems (DCOP). This article attempts to state the smart home coordination problem in general terms, and provides the details of a DCOP‐based approach by describing a case study taken from theRoboCareproject. More specifically, we show how (1) DCOP is a convenient metaphor for casting smart home coordination problems, and (2) the specific features which distinguishAdopt‐Nfrom other algorithms for DCOP represent a strong asset in the smart home domain.

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate

A module for quantitative temporal management is presented which can be easily connected with different problem solving architectures. Particular attention has been devoted to planning and scheduling problems in realistic domains. The module allows for an incremental constraint posting approach for building solutions. After introducing the consistency-checking problem in quantitative temporal networks, a correct and complete algorithm for constraint propagation is described, and a sufficient condition for inconsistency is also presented as useful to improve the algorithm. Moreover a repropagate operator is given which is used to re-establish a consistent network when a given constraint set is relaxed.

The authors describe HSTS, an integrated planning and scheduling architecture that has been applied to the problem of generating observation schedules for the Hubble Space Telescope. HSTS deals with the problem of the interaction of resource allocation and auxiliary task expansion during schedule development by viewing planning and scheduling as two complementary aspects in the construction of the behavior of a system. The authors first describe how HSTS specifies the dynamics of a system, how it represents schedules at multiple levels of abstraction, and the specific problem solving machinery it provides. An example of the use of the architecture in the Hubble Space Telescope domain is given. Performance results that indicate the practicality of the HSTS approach are presented.>