Papers by Vincenzo Lippiello
International Journal of Control, Automation and Systems, 2022
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2015 23rd Mediterranean Conference on Control and Automation (MED), 2015
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2011 IEEE International Conference on Robotics and Automation (ICRA 2011), 2011
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Depalletizing robotic systems are commonly de- ployed to automatize and speed up parts of logisti... more Depalletizing robotic systems are commonly de- ployed to automatize and speed up parts of logistic processes. De- spite this, the necessity to adapt the preexisting logistic processes to the automatic systems often impairs the application of such robotic solutions to small business realities like supermarkets. Integrating a robotic system into the supermarket depalletizing process demands a high level of autonomy, based on strong perceptive, executive and gripping capabilities. This abstract describes an integrated robotic depalletizing system designed to be easily deployed into supermarket logistic processes. The system is described along with its main components, showing how the proposed framework performs in a real supermarket scenario.
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Springer Tracts in Advanced Robotics, 2022
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Springer Tracts in Advanced Robotics, 2022
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2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019
Cobots (Collaborative robots) are increasingly employed in industrial workplaces, assisting human... more Cobots (Collaborative robots) are increasingly employed in industrial workplaces, assisting human operators in decreasing the weight and the repetitiveness of their activities. In order to promote the acceptance of cobots, novel interaction paradigms enabling intuitive collaboration between humans and robots are needed. In this work, we propose a variable admittance control framework based on virtual fixtures, in which the damping and the stiffness of the admittance controller are on-line adjusted to increase the effectiveness of the co-manipulation task. Virtual paths are generated to support and guide the operator during collaborative task execution, providing him/her an over-responsive compliant system when the robot is far from the targets and a precise heavy tool in their proximity. The proposed approach has been compared with a fixed admittance controller in an industrial use case consisting of a human operator interacting with a Kuka LBR IIWA arm. The collected results demonstrate the effectiveness of the proposed approach.
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ArXiv, 2016
We present a multimodal interaction framework suitable for a human rescuer that operates in proxi... more We present a multimodal interaction framework suitable for a human rescuer that operates in proximity with a set of co-located drones during search missions. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse, incomplete, although high-value, instructions to the robots. This operative scenario requires a human-interaction framework that supports multimodal communication along with an effective and natural mixed-initiative interaction between the human and the robots. In this work, we illustrate the domain and the proposed multimodal interaction framework discussing the system at work in a simulated case study.
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2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2017
We present a novel multimodal interaction framework supporting robust human-robot communication. ... more We present a novel multimodal interaction framework supporting robust human-robot communication. We consider a scenario where a human operator can exploit multiple communication channels to interact with one or more robots in order to accomplish shared tasks. Moreover, we assume that the human is not fully dedicated to the robot control, but also involved in other activities, hence only able to interact with the robotic system in a sparse and incomplete manner. In this context, several human or environmental factors could cause errors, noise and wrong interpretations of the commands. The main goal of this work is to improve the robustness of humanrobot interaction systems in similar situations. In particular, we propose a multimodal fusion method based on the following steps: for each communication channel, unimodal classifiers are firstly deployed in order to generate unimodal interpretations of the human inputs; the unimodal outcomes are then grouped into different multimodal recognition lines, each representing a possible interpretation of a sequence of multimodal inputs; these lines are finally assessed in order to recognize the human commands. We discuss the system at work in a case study in which a human rescuer interacts with a team of flying robots during Search & Rescue missions. In this scenario, we present and discuss real world experiments to demonstrate the effectiveness of the proposed framework.
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Fault Diagnosis and Fault-tolerant Control of Robotic and Autonomous Systems, 2020
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Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics, 2018
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IFAC-PapersOnLine, 2017
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2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2015
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IEEE Robotics and Automation Letters, 2016
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IEEE Transactions on Robotics, 2015
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2015 23rd Mediterranean Conference on Control and Automation (MED), 2015
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IEEE Robotics and Automation Letters, 2018
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Proceedings of the 16th IFAC World Congress, 2005, 2005
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2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2013
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2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2013
ABSTRACT This paper proposes two methods for UAV translational velocity estimation based on onboa... more ABSTRACT This paper proposes two methods for UAV translational velocity estimation based on onboard sensing only. Spherical image measurements provided by a single onboard camera along with IMU data consist the main information feeding the estimators. The first algorithm consists of a nonlinear observer, designed using Lyapunov synthesis, while the second is based on the Unscented Kalman filtering technique. Differently with respect to existing approaches, the velocity is directly estimated from the onboard image without the need to fully estimate the vehicle 3D pose. The low computational requirement makes the proposed techniques suitable for applications where the execution time is of prominent importance even if no powerful hardware is available, as it is the case with UAV systems. Experimental results validate the algorithms, and this with the use of only four image features.
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Papers by Vincenzo Lippiello