00462nas a2200145 4500008004100000245006800041210006700109490000700176100001400183700001700197700001500214700001900229700001700248856005100265 2020 eng d00aLiDAR-Based GNSS Denied Localization for Autonomous Racing Cars0 aLiDARBased GNSS Denied Localization for Autonomous Racing Cars0 v201 aMassa, F.1 aBonamini, L.1 aSettimi, A1 aPallottino, L.1 aCaporale, D. uhttps://www.mdpi.com/1424-8220/20/14/3992#cite01186nas a2200373 4500008004100000022001400041245012200055210006900177260001000246300001400256490000600270653002600276653001200302653004500314653001300359653002900372653001300401653002700414653002000441653002600461653001600487653001100503100002000514700001400534700001500548700001600563700001400579700001500593700002000608700001500628700001500643700001600658856013800674 2019 eng d a2377-376600aLearning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands0 aLearning From Humans How to Grasp A DataDriven Architecture for cApril a1533-15400 v410aComputer architecture10aControl10aDeep Learning in Robotics and Automation10aGrasping10aLearning for Soft Robots10aModeling10aNatural Machine Motion10aNeural networks10aRobot sensing systems10aUncertainty10aVideos1 aSantina, C., D.1 aArapi, V.1 aAverta, G.1 aDamiani, F.1 aFiore, G.1 aSettimi, A1 aCatalano, M. G.1 aBacciu, D.1 aBicchi, A.1 aBianchi, M. uhttp://www.centropiaggio.unipi.it/publications/learning-humans-how-grasp-data-driven-architecture-autonomous-grasping-anthropomorphic00642nas a2200181 4500008004100000245008200041210006900123100001700192700001500209700001400224700001700238700001400255700001700269700001700286700001500303700001900318856012300337 2019 eng d00aTowards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars0 aTowards the Design of Robotic Drivers for FullScale SelfDriving 1 aCaporale, D.1 aSettimi, A1 aMassa, F.1 aAmerotti, F.1 aCorti, A.1 aFagiolini, A1 aGuiggiani, M1 aBicchi, A.1 aPallottino, L. uhttp://www.centropiaggio.unipi.it/publications/towards-design-robotic-drivers-full-scale-self-driving-racing-cars.html01088nas a2200373 4500008004100000022001400041245007100055210006900126300000800195653001400203653001600217653001300233653002200246653002600268653001800294100001700312700001500329700001700344700001600361700001700377700001700394700001700411700001600428700001700444700001900461700001500480700001900495700002000514700001800534700001500552700001700567700002000584856011000604 2018 eng d a1070-993200aHumanoids at Work: The WALK-MAN Robot in a Postearthquake Scenario0 aHumanoids at Work The WALKMAN Robot in a Postearthquake Scenario a1-110aBuildings10aEarthquakes10aHardware10aLegged locomotion10aRobot sensing systems10aTask analysis1 aNegrello, F.1 aSettimi, A1 aCaporale, D.1 aLentini, G.1 aPoggiani, M.1 aKanoulas, D.1 aMuratore, L.1 aLuberto, E.1 aSantaera, G.1 aCiarleglio, L.1 aErmini, L.1 aPallottino, L.1 aCaldwell, D. G.1 aTsagarakis, N1 aBicchi, A.1 aGarabini, M.1 aCatalano, M. G. uhttp://www.centropiaggio.unipi.it/publications/humanoids-work-walk-man-robot-postearthquake-scenario.html00696nas a2200217 4500008003900000245006700039210006400106260002500170300000800195100001700203700001700220700001900237700001500256700001500271700001700286700001400303700001600317700001400333700001800347856011300365 2018 d00aA Planning and Control System for Self-Driving Racing Vehicles0 aPlanning and Control System for SelfDriving Racing Vehicles aPalermo, ItalycSept a1-61 aCaporale, D.1 aFagiolini, A1 aPallottino, L.1 aSettimi, A1 aBiondo, A.1 aAmerotti, F.1 aMassa, F.1 aDe Caro, S.1 aCorti, A.1 aVenturini, L. uhttp://www.centropiaggio.unipi.it/publications/planning-and-control-system-self-driving-racing-vehicles.html01162nas a2200457 4500008004100000245003100041210003000072260001300102300001400115490000800129100002300137700001700160700001700177700001300194700002000207700001600227700001600243700002000259700001600279700001700295700001600312700001900328700001500347700001400362700001500376700001700391700001700408700001900425700001500444700001600459700001600475700001500491700001900506700001900525700001400544700001100558700001700569700002000586700001500606856008300621 2018 eng d00aWALK-MAN Humanoid Platform0 aWALKMAN Humanoid Platform bSpringer a495–5480 v1211 aTsagarakis, N., G.1 aNegrello, F.1 aGarabini, M.1 aChoi, W.1 aBaccelliere, L.1 aLoc, V., G.1 aNoorden, J.1 aCatalano, M. G.1 aFerrati, M.1 aMuratore, L.1 aKryczka, P.1 aHoffman, Mingo1 aSettimi, A1 aRocchi, A1 aMargan, A.1 aCordasco, S.1 aKanoulas, D.1 aCardellino, A.1 aNatale, L.1 aDallali, H.1 aMalzahn, J.1 aKashiri, N1 aVarricchio, V.1 aPallottino, L.1 aPavan, C.1 aLee, J1 aAjoudani, A.1 aCaldwell, D. G.1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/walk-man-humanoid-platform.html03800nas a2200481 4500008004100000022001400041245007800055210006900133260001200202300001100214490000700225520254400232653001302776100002302789700002002812700001702832700001302849700002002862700001602882700001602898700001702914700001502931700001902946700001502965700001902980700001602999700001503015700001603030700001103046700001603057700001703073700001703090700002003107700001603127700001903143700001903162700001403181700001503195700001503210700001403225700001703239856006203256 2017 eng d a1556-496700aWALK-MAN: A High-Performance Humanoid Platform for Realistic Environments0 aWALKMAN A HighPerformance Humanoid Platform for Realistic Enviro c06/2017 a1 - 340 v343 a
In this work, we present WALK-MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high-strength capabilities, and physical sturdiness. To enable these capabilities, WALK-MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state-of-the-art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK-MAN actuation that combines customized high-performance modules with tuned torque/velocity curves and transmission elasticity for high-speed adaptation response and motion reactions to disturbances. WALK-MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK-MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK-MAN is based on the unified motion-generation framework of whole-body locomotion and manipulation (termed loco-manipulation). WALK-MAN is able to execute simple loco-manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion-generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.
10aRobotics1 aTsagarakis, N., G.1 aCaldwell, D. G.1 aNegrello, F.1 aChoi, W.1 aBaccelliere, L.1 aLoc, V., G.1 aNoorden, J.1 aMuratore, L.1 aMargan, A.1 aCardellino, A.1 aNatale, L.1 aHoffman, Mingo1 aDallali, H.1 aKashiri, N1 aMalzahn, J.1 aLee, J1 aKryczka, P.1 aKanoulas, D.1 aGarabini, M.1 aCatalano, M. G.1 aFerrati, M.1 aVarricchio, V.1 aPallottino, L.1 aPavan, C.1 aBicchi, A.1 aSettimi, A1 aRocchi, A1 aAjoudani, A. uhttp://onlinelibrary.wiley.com/doi/10.1002/rob.21702/epdf01788nas a2200193 4500008003900000245011800039210006900157260004400226300001400270490006900284520107100353653002101424653001301445100001901458700001701477700001501494700001901509856006601528 2016 d00aAPRICOT: Aerospace PRototypIng COntrol Toolbox. A Modeling and Simulation Environment for Aircraft Control Design0 aAPRICOT Aerospace PRototypIng COntrol Toolbox A Modeling and Sim aRome, Italy, June 15-16, 2016bSpringer a139 - 1570 v9991 of the book series Lecture Notes in Computer Science (LNCS)3 aA novel MATLAB/Simulink based modeling and simulation environment for the design and rapid prototyping of state-of-the-art aircraft control systems is proposed. The toolbox, named APRICOT, is able to simulate the longitudinal and laterodirectional dynamics of an aircraft separately, as well as the complete 6 degrees of freedom dynamics. All details of the dynamics can be easily customized in the toolbox, some examples are shown in the paper. Moreover, different aircraft models can be easily integrated. The main goal of APRICOT is to provide a simulation environment to test and validate different control laws with different aircraft models. Hence, the proposed toolbox has applicability both for educational purposes and control rapid prototyping. With respect to similar software packages, APRICOT is customizable in all its aspects, and has been released as open source software. An interface with Flightgear Simulator allows for online visualization of the flight. Examples of control design with simulation experiments are reported and commented.
10aEmbedded Control10aRobotics1 aFerrarelli, A.1 aCaporale, D.1 aSettimi, A1 aPallottino, L. uhttp://link.springer.com/chapter/10.1007/978-3-319-47605-6_1101788nas a2200181 4500008003900000020002300039245007300062210007100135260004300206520121200249653001301461100001501474700001701489700001601506700001601522700001901538856004901557 2016 d a978-1-5090-4718-5 00aMotion Primitive Based Random Planning for Loco–Manipulation Tasks0 aMotion Primitive Based Random Planning for Loco–Manipulation Tas aCancun, Mexico, 15-17 Nov. 2016 bIEEE3 aSeveral advanced control laws are available for
complex robotic systems such as humanoid robots and mobile
manipulators. Controls are usually developed for locomotion or
for manipulation purposes. Resulting motions are usually executed
sequentially and the potentiality of the robotic platform
is not fully exploited.
In this work we consider the problem of loco–manipulation
planning for a robot with given parametrized control laws
known as primitives. Such primitives, may have not been
designed to be executed simultaneously and by composing
them instability may easily arise. With the proposed approach,
primitives combination that guarantee stability of the system
are obtained resulting in complex whole–body behavior.
A formal definition of motion primitives is provided and a
random sampling approach on a manifold with limited dimension
is investigated. Probabilistic completeness and asymptotic
optimality are also proved. The proposed approach is tested
both on a mobile manipulator and on the humanoid robot
Walk-Man, performing loco–manipulation tasks.
The purpose of this work is to move a step toward the automation of industrial plants through full exploitation of autonomous robots. A planning algorithm is proposed to move different objects in desired configurations with heterogeneous robots such as manipulators, mobile robots and conveyor belts.
The proposed approach allows different objects to be handled by different robots simultaneously in an efficient way and avoiding collisions with the environment and self–collisions between robots. In particular, the integrated system will be capable of planning paths for a set of objects from various starting points in the environment (e.g. shelves) to their respective final destinations. The proposed approach unifies the active (e.g., grasping by a hand) and passive (e.g., holding by a table) steps involved in moving the objects in the environment by treating them as end–effectors with constraints and capabilities.
Time varying graphs will be introduced to model the problem for simultaneous handling of objects by different end–effectors.
Optimal exploration of such graphs will be used to determine paths for each object with time constraints. Results will be validated through simulations.
Moving objects with autonomous robots is a wide topic that includes single-arm pick-and-place tasks, object regrasping, object passing between two or more arms in the air or using support surfaces such as tables and similar. Each task has been extensively studied and many planning solutions are already present in the literature. In this letter, we present a planning scheme which, based on the use of pre-defined elementary manipulation skills, aims to unify solutions which are usually obtained by means of different planning strategies rooted on hardcoded behaviors. Both robotic manipulators and environment fixed support surfaces are treated as end-effectors of movable and non-movable types, respectively. The task of the robot can thus be broken down into elementary building blocks, which are endeffector manipulation skills, that are then planned at the kinematic level. Feasibility is ensured by propagating unforeseen low-level failures at the higher level and by synthesizing different behaviors. The validity of the proposed solution is shown via experiments on a bimanual robot setup and in simulations involving a more complex setup similar to an assembly line.
10aRobotics1 aMarino, H1 aFerrati, M.1 aSettimi, A1 aRosales, C1 aGabiccini, M uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=738469401869nas a2200181 4500008004100000245004500041210004000086260001200126520140500138653001301543100001601556700001501572700001701587700002301604700001501627700001901642856002601661 2016 eng d00aThe Walk-Man Robot Software Architecture0 aWalkMan Robot Software Architecture c05/20163 aA software and control architecture for a humanoid robot is a complex and large project, which involves a team of developers/researchers to be coordinated and requires many hard design choices. If such project has to be done in a very limited time, i.e., less than 1 year, more constraints are added and concepts, such as modular design, code reusability, and API definition, need to be used as much as possible. In this work, we describe the software architecture developed for Walk-Man, a robot participant at the Darpa Robotics Challenge. The challenge required the robot to execute many different tasks, such as walking, driving a car, and manipulating objects. These tasks need to be solved by robotics specialists in their corresponding research field, such as humanoid walking, motion planning, or object manipulation. The proposed architecture was developed in 10 months, provided boilerplate code for most of the functionalities required to control a humanoid robot and allowed robotics researchers to produce their control modules for DRC tasks in a short time. Additional capabilities of the architecture include firmware and hardware management, mixing of different middlewares, unreliable network management, and operator control station GUI. All the source code related to the architecture and some control modules have been released as open source projects.
10aRobotics1 aFerrati, M.1 aSettimi, A1 aMuratore, L.1 aTsagarakis, N., G.1 aNatale, L.1 aPallottino, L. uhttp://bit.ly/2jAPke200664nas a2200169 4500008003900000245007700039210006900116260003300185300001200218490005800230653002100288653001300309100001600322700001500338700001900353856012200372 2014 d00aASCARI: a component based simulator for distributed mobile robot systems0 aASCARI a component based simulator for distributed mobile robot aRome, 5-6 May 2014bSpringer a152-1630 v Lecture Notes in Computer Science, Volume 8906, 201410aEmbedded Control10aRobotics1 aFerrati, M.1 aSettimi, A1 aPallottino, L. uhttp://www.centropiaggio.unipi.it/publications/ascari-component-based-simulator-distributed-mobile-robot-systems.html02577nas a2200241 4500008003900000245009300039210006900132260004200201300001400243520183800257653001202095653001302107100001702120700001102137700001402148700001602162700001902178700001502197700002002212700001502232700002002247856006802267 2014 d00aManipulation Framework for Compliant Humanoid COMAN: Application to a Valve Turning Task0 aManipulation Framework for Compliant Humanoid COMAN Application aMadrid, Spain, November 18 - 20bIEEE a664 - 6703 aWith the purpose of achieving a desired interaction performance for our compliant humanoid robot (COMAN), in this paper we propose a semi-autonomous control framework and evaluate it experimentally in a valve turning setup. The control structure consists of various modules and interfaces to identify the valve, locate the robot in front of it and perform the manipulation. The manipulation module implements four motion primitives (Reach, Grasp, Rotate and Disengage) and realizes the corresponding desired impedance profile for each phase to accomplish the task. In this direction, to establish a stable and compliant contact between the valve and the robot hands, while being able to generate the sufficient rotational torques depending on the valve's friction, Rotate incorporates a novel dual-arm impedance control technique to plan and realize a task-appropriate impedance profile. Results of the implementation of the proposed control framework are firstly evaluated in simulation studies using Gazebo. Subsequent experimental results highlight the efficiency of the proposed impedance planning and control in generation of the required interaction forces to accomplish the task.
10aHaptics10aRobotics1 aAjoudani, A.1 aLee, J1 aRocchi, A1 aFerrati, M.1 aHoffman, Mingo1 aSettimi, A1 aCaldwell, D. G.1 aBicchi, A.1 aTsagarakis, N G uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=704143400719nas a2200205 4500008003900000245009500039210006900134260002300203653001300226100001500239700001400254700001900268700001600287700001400303700001400317700001300331700002000344700001500364856013400379 2014 d00aA modular approach for remote operation of humanoid robots in search and rescue scenarios 0 amodular approach for remote operation of humanoid robots in sear aRome, 5-6 May 201410aRobotics1 aSettimi, A1 aPavan, C.1 aVarricchio, V.1 aFerrati, M.1 aMingo, E.1 aRocchi, A1 aMelo, K.1 aTsagarakis, N G1 aBicchi, A. uhttp://www.centropiaggio.unipi.it/publications/modular-approach-remote-operation-humanoid-robots-search-and-rescue-scenarios.html02368nas a2200229 4500008003900000245009500039210006900134260004200203300001400245520165100259653001301910100001101923700001701934700001901951700001401970700001501984700001601999700001502015700002002030700002002050856006802070 2014 d00aUpper-body Impedance Control with an Intuitive Stiffness Emulation for a Door Opening Task0 aUpperbody Impedance Control with an Intuitive Stiffness Emulatio aMadrid, Spain, November 18 - 20bIEEE a713 - 7193 aThe advent of humanoids has brought new challenges in the real-world application. As a part of ongoing efforts to foster functionality of the robot accommodating a real environment, this paper introduces a recent progress on a door opening task with our compliant humanoid, CoMan. We presents a task-prioritized impedance control framework for an upper body system that includes a dual-arm, a waist, two soft hands, and 3D camera. Aimed to create desired responses to open the door, a novel stiffness modulation method is proposed, incorporating a realtime optimization. As a preliminary experiment, a full door-opening scenario (approaching to the door and reaching, grasping, rotating and pulling the door handle) is demonstrated under a semi-autonomous operation with a pilot. The experimental result shows the effectiveness and efficacy of the proposed impedance control approach. Despite of uncertainties from sensory data, the door opening task is successfully achieved and safe and robust interaction is established without creating excessive forces.
10aRobotics1 aLee, J1 aAjoudani, A.1 aHoffman, Mingo1 aRocchi, A1 aSettimi, A1 aFerrati, M.1 aBicchi, A.1 aTsagarakis, N G1 aCaldwell, D. G. uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=704144100632nas a2200217 4500008003900000245004400039210004400083260003100127653001300158100001300171700001800184700001400202700001600216700001500232700001400247700001400261700001500275700001200290700002000302856009200322 2014 d00aYarp Based Plugins for Gazebo Simulator0 aYarp Based Plugins for Gazebo Simulator aRoma, Italy, 5 -6 May 201410aRobotics1 aMingo, E1 aTraversaro, S1 aRocchi, A1 aFerrati, M.1 aSettimi, A1 aRomano, F1 aNatale, L1 aBicchi, A.1 aNori, F1 aTsagarakis, N G uhttp://www.centropiaggio.unipi.it/publications/yarp-based-plugins-gazebo-simulator.html00558nas a2200133 4500008003900000245009800039210006900137260002000206300001600226653001300242100001500255700001900270856013500289 2013 d00aA Subgradient Based Algorithm for Distributed Task Assignment for Heterogeneous Mobile Robots0 aSubgradient Based Algorithm for Distributed Task Assignment for aFlorence, Italy a3665 - 367010aRobotics1 aSettimi, A1 aPallottino, L. uhttp://www.centropiaggio.unipi.it/publications/subgradient-based-algorithm-distributed-task-assignment-heterogeneous-mobile-robots