Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation

Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them

Teaching humanoid robotics by means of human teleoperation through RGB-D sensors

This paper presents a graduate course project on humanoid robotics offered by the University of Padova. The target is to safely lift an object by teleoperating a small humanoid. Students have to map human limbs into robot joints, guarantee the robot stability during the motion, and teleoperate the robot to perform the correct movement. We introduce the following innovative aspects with respect to classical robotic classes: i) the use of humanoid robots as teaching tools; ii) the simplification of the stable locomotion problem by exploiting the potential of teleoperation; iii) the adoption of a Project-Based Learning constructivist approach as teaching methodology. The learning objectives of both course and project are introduced and compared with the students\u2019 background. Design and constraints students have to deal with are reported, together with the amount of time they and their instructors dedicated to solve tasks. A set of evaluation results are provided in order to validate the authors\u2019 purpose, including the students\u2019 personal feedback. A discussion about possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels

Proprioceptive Robot Collision Detection through Gaussian Process Regression

This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.Comment: Published at ACC 201

RUR53: an Unmanned Ground Vehicle for Navigation, Recognition and Manipulation

This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously navigate through, identify, and reach areas of interest; and there recognize, localize, and manipulate work tools to perform complex manipulation tasks. The proposed contribution includes a modular software architecture where each module solves specific sub-tasks and that can be easily enlarged to satisfy new requirements. Included indoor and outdoor tests demonstrate the capability of the proposed system to autonomously detect a target object (a panel) and precisely dock in front of it while avoiding obstacles. They show it can autonomously recognize and manipulate target work tools (i.e., wrenches and valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve stem). A specific case study is described where the proposed modular architecture lets easy switch to a semi-teleoperated mode. The paper exhaustively describes description of both the hardware and software setup of RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International Robotics Challenge, and the lessons we learned when participating at this competition, where we ranked third in the Gran Challenge in collaboration with the Czech Technical University in Prague, the University of Pennsylvania, and the University of Lincoln (UK).Comment: This article has been accepted for publication in Advanced Robotics, published by Taylor & Franci

Cognitive Task Planning for Smart Industrial Robots

This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm. The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents. Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty. The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties. Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions. The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them.Questa ricerca presenta una nuova struttura di Pianificazione Cognitiva delle Attività ideata per Robot Industriali Intelligenti. La struttura rende Cognitivo un manipolatore industriale mobile applicando le tecnologie offerte dal Web Semantico. Viene inoltre introdotto un nuovo algoritmo di Navigazione tra Oggetti Removibili per robot che navigano e manipolano all’interno di una fabbrica. L’obiettivo di Industria 4.0 è quello di creare Fabbriche Intelligenti: fabbriche modulari dotate di sistemi cyber-fisici in grado di customizzare i prodotti pur mantenendo una produzione di massa altamente flessibile. Tali sistemi devono essere in grado di comunicare e cooperare tra loro e con gli agenti umani in tempo reale, attraverso l’Internet delle Cose. Devono sapersi autonomamente ed intelligentemente adattare ai costanti cambiamenti dell’ambiente che li circonda. Devono saper navigare autonomamente all’interno della fabbrica, anche spostando ostacoli che occludono percorsi liberi, ed essere in grado di manipolare questi oggetti anche se visti per la prima volta. Devono essere in grado di imparare dalle loro azioni e da quelle eseguite da altri agenti. La maggior parte dei robot industriali mobili naviga secondo traiettorie generate a priori. Seguono filielettrificatiincorporatinelterrenoolineedipintesulpavimento. Pianificareapriorièfunzionale se l’ambiente è immutevole e i cicli produttivi sono caratterizzati da criticità temporali. E’ preferibile adottare una pianificazione dinamica se, invece, l’area di lavoro ed i compiti assegnati cambiano frequentemente: i robot devono saper navigare autonomamente senza tener conto dei cambiamenti circostanti. Si consideri il comportamento umano: l’uomo ragiona sulla possibilità di spostare ostacolise unaposizione obiettivo nonè raggiungibileose talespostamento puòaccorciare la traiettoria da percorrere. Questo problema viene detto Navigazione tra Oggetti Removibili ed è noto alla robotica di soccorso. Questo lavoro traspone il problema in uno scenario industriale e prova ad affrontare i suoi due obiettivi principali: l’elevata dimensione dello spazio di ricerca ed il trattamento dell’incertezza. L’algoritmo proposto vuole dare priorità di esplorazione alle aree meno esplorate, per questo estende l’algoritmo noto come Kinodynamic Motion Planning by Interior-Exterior Cell Exploration. L’estensione non impone l’elusione degli ostacoli. Assegna ad ogni cella un’importanza che combina lo sforzo necessario per raggiungerla con quello necessario per liberarla da eventuali ostacoli. L’algoritmo risultante è scalabile grazie alla sua indipendenza dalla dimensione della mappa e dal numero, forma e posizione degli ostacoli. Non impone restrizioni sulle azioni da eseguire: ogni oggetto può venir spinto o afferrato. Allo stato attuale, l’algoritmo assume una completa conoscenza del mondo circonstante. L’ambiente è però riconfigurabile di modo che l’algoritmo possa venir facilmente esteso alla risoluzione di problemi di Navigazione tra Oggetti Removibili in ambienti ignoti. L’algoritmo gestisce i feedback dati dai sensori per correggere le incertezze. Solitamente la Robotica separa la risoluzione dei problemi di pianificazione del movimento da quelli di manipolazione. La Navigazione tra Ostacoli Removibili forza il loro trattamento combinato introducendo la necessità di manipolare oggetti diversi, spesso ignoti, durante la navigazione. Adottare prese pre calcolate non fa fronte alla grande quantità e diversità di oggetti esistenti. Questa tesi propone un Framework di Conoscenza Semantica a supporto dell’algoritmo sopra esposto. Essodàairobotlacapacitàdiimparareamanipolareoggettiedisseminareleinformazioni acquisite durante il compimento dei compiti assegnati. Il Framework si compone di un’Ontologia e di un Engine. L’Ontologia estende lo Standard IEEE formulato per Ontologie per la Robotica e l’Automazione andando a definire le manipolazioni apprese e gli oggetti rilevati. È accessibile a qualsiasi robot connesso al Cloud. Può venir considerato I) una raccolta di dati per l’esecuzione efficiente ed affidabile di azioni ripetute; II) un archivio Web per lo scambio di informazioni tra robot e la velocizzazione della fase di apprendimento. Ad ora, non esistono altre ontologie sulla manipolazione che rispettino lo Standard IEEE. Indipendentemente dallo standard, l’Ontologia propostadifferiscedaquelleesistentiperiltipodiinformazionisalvateeperilmodoefficienteincui un agente può accedere a queste informazioni: attraverso un algoritmo di Cascade Hashing molto veloce. L’Engine consente il calcolo e il salvataggio delle manipolazioni non ancora in Ontologia. Si basa su tecniche di Reinforcement Learning che evitano il training massivo su basi di dati a larga scala, favorendo l’interazione uomo-robot. Infatti, viene data ai robot la possibilità di imparare dagli umani attraverso un framework di Apprendimento Robotico da Dimostrazioni. Il sistema finale è flessibile ed adattabile a robot diversi operanti in diversi ambienti industriali. È caratterizzato da una struttura modulare in cui ogni blocco è completamente riutilizzabile. Ogni blocco si basa sul sistema open-source denominato Robot Operating System. Non tutti i controllori industriali sono disegnati per essere compatibili con questa piattaforma. Viene quindi presentato il metodo che è stato adottato per aprire i controllori dei robot industriali e crearne un’interfaccia ROS

Motion Planning per un robot a molti gradi di libertà

il lavoro vuole risolvere un problema di Motion Planning per un robot umanoide, il Robovie-X, in un ambiente popolato da ostacoli. A tal scopo sono stati usati ROS e le sue librerie, tre le più importanti OMPL e SBPL. È stato usato Gazebo ed il robot è stato portato sotto questo simulatore. Sono state studiate dinamica e cinematica di un robot umanoid

An Integrated System to approach the Programming of Humanoid Robotics

This paper describes a set of laboratory experiences focused on humanoid robots offered at the University of Padua. Instructors developed an integrated system through which students can work with robots. The aim is to improve the educational experience introducing a new learning tool, namely a humanoid robot, and the Robots Operating System (ROS) in a constructivist framework. This approach to robotics teaching lets students exploiting up-to-date robotic technologies and to deal with multidisciplinary problems, applying a scientic approach. By using humanoid robots, students are able to compare human movements to robot motion. The comparison brings out human/robot similarities, pushing students to solve complex motion problems in a more natural way while discovering robot limitations. In this paper, the learning objectives of the project, and the tools used by the students are presented. A set of evaluation results are provided in order to validate the authors' purpose. Finally, a discussion about designed experiences and possible future improvements is reported, hoping to encourage further spread of educational robotics in schools at all levels