Author
Paolo Robuffo Giordano
Other affiliations: German Aerospace Center, Max Planck Society, Centre national de la recherche scientifique ...read more
Bio: Paolo Robuffo Giordano is an academic researcher from University of Rennes. The author has contributed to research in topics: Teleoperation & Haptic technology. The author has an hindex of 40, co-authored 128 publications receiving 4526 citations. Previous affiliations of Paolo Robuffo Giordano include German Aerospace Center & Max Planck Society.
Topics: Teleoperation, Haptic technology, Visual servoing, Mobile robot, Robot
Papers published on a yearly basis
Papers
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TL;DR: The additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle.
Abstract: Standard quadrotor unmanned aerial vehicles (UAVs) possess a limited mobility because of their inherent underactuation, that is, availability of four independent control inputs (the four propeller spinning velocities) versus the 6 degrees of freedom parameterizing the quadrotor position/orientation in space. Thus, the quadrotor pose cannot track arbitrary trajectories in space (e.g., it can hover on the spot only when horizontal). Because UAVs are more and more employed as service robots for interaction with the environment, this loss of mobility due to their underactuation can constitute a limiting factor. In this paper, we present a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations. Indeed, the additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle. We then develop a comprehensive modeling and control framework for the proposed quadrotor, and subsequently illustrate the hardware and software specifications of an experimental prototype. Finally, the results of several simulations and real experiments are reported to illustrate the capabilities of the proposed novel UAV design.
299 citations
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14 May 2012TL;DR: This paper proposes a novel actuation concept in which the quadrotor propellers are allowed to tilt about their axes w.r.t. the main Quadrotor body, and proposes a nonlinear trajectory tracking controller based on dynamic feedback linearization techniques.
Abstract: Standard quadrotor UAVs possess a limited mobility because of their inherent underactuation, i.e., availability of 4 independent control inputs (the 4 propeller spinning velocities) vs. the 6 dofs parameterizing the quadrotor position/ orientation in space. As a consequence, the quadrotor pose cannot track an arbitrary trajectory over time (e.g., it can hover on the spot only when horizontal). In this paper, we propose a novel actuation concept in which the quadrotor propellers are allowed to tilt about their axes w.r.t. the main quadrotor body. This introduces an additional set of 4 control inputs which provides full actuation to the quadrotor position/orientation. After deriving the dynamical model of the proposed quadrotor, we formally discuss its controllability properties and propose a nonlinear trajectory tracking controller based on dynamic feedback linearization techniques. The soundness of our approach is validated by means of simulation results.
227 citations
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Abstract: Robustness and flexibility constitute the main advantages of multiple-robot systems with respect to single-robot ones as per the recent literature. The use of multiple unmanned aerial vehicles (UAVs) combines these benefits with the agility and pervasiveness of aerial platforms [1], [2]. The degree of autonomy of the multi-UAV system should be tuned according to the specificities of the situation under consideration. For regular missions, fully autonomous UAV systems are often appropriate, but, in general, the use of semiautonomous groups of UAVs, supervised or partially controlled by one or more human operators, is the only viable solution to deal with the complexity and unpredictability of real-world scenarios as in, e.g., the case of search and rescue missions or exploration of large/cluttered environments [3]. In addition, the human presence is also mandatory for taking the responsibility of critical decisions in high-risk situations [4].
216 citations
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TL;DR: A rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study and provides an experimental validation with four quadrotor UAVs.
Abstract: In this paper, a novel decentralized control strategy for bilaterally teleoperating heterogeneous groups of mobile robots from different domains (aerial, ground, marine, and underwater) is proposed. By using a decentralized control architecture, the group of robots, which is treated as the slave side, is made able to navigate in a cluttered environment while avoiding obstacles, interrobot collisions, and following the human motion commands. Simultaneously, the human operator acting on the master side is provided with a suitable force feedback informative of the group response and of the interaction with the surrounding environment. Using passivity-based techniques, we allow the behavior of the group to be as flexible as possible with arbitrary split and join events (e.g., due to interrobot visibility/packet losses or specific task requirements) while guaranteeing the stability of the system. We provide a rigorous analysis of the system stability and steady-state characteristics and validate performance through human/hardware-in-the-loop simulations by considering a heterogeneous fleet of unmanned aerial vehicles (UAVs) and unmanned ground vehicles as a case study. Finally, we also provide an experimental validation with four quadrotor UAVs.
189 citations
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TL;DR: In this paper, a semiautonomous haptic teleoperation control architecture for multiple UAVs is proposed, consisting of three control layers: 1) UAV control layer, where each UAV is abstracted by, and is controlled to follow the trajectory of its own kinematic Cartesian virtual point (VP); 2) VP controller layer, which modulates each VP's motion according to the teleoperation commands and local artificial potentials (VP-VP/VP-obstacle collision avoidance and VP-VP connectivity preservation); and 3) teleoperation layer, through which
Abstract: We propose a novel semiautonomous haptic teleoperation control architecture for multiple unmanned aerial vehicles (UAVs), consisting of three control layers: 1) UAV control layer, where each UAV is abstracted by, and is controlled to follow the trajectory of, its own kinematic Cartesian virtual point (VP); 2) VP control layer, which modulates each VP's motion according to the teleoperation commands and local artificial potentials (for VP-VP/VP-obstacle collision avoidance and VP-VP connectivity preservation); and 3) teleoperation layer, through which a single remote human user can command all (or some) of the VPs' velocity while haptically perceiving the state of all (or some) of the UAVs and obstacles. Master passivity/slave stability and some asymptotic performance measures are proved. Experimental results using four custom-built quadrotor-type UAVs are also presented to illustrate the theory.
180 citations
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TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:
1,829 citations
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TL;DR: In this article, the authors tried to read modelling and control of robot manipulators as one of the reading material to finish quickly, and they found that reading book can be a great choice when having no friends and activities.
Abstract: Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.
517 citations
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TL;DR: The proposed DRL-EC3 maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity, and makes decisions under the guidance of two powerful deep neural networks.
Abstract: Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.
412 citations
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TL;DR: A survey regarding the potential use of UAVs in PA is provided, focusing on 20 relevant applications, which investigate in detail 20 UAV applications that are devoted to either aerial crop monitoring processes or spraying tasks.
386 citations
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TL;DR: Various path planning techniques for UAVs are classified into three broad categories, i.e., representative techniques, cooperative techniques, and non-cooperative techniques, with these techniques, coverage and connectivity of the UAV's network communication are discussed and analyzed.
359 citations