Showing papers by "Marco Tognon published in 2021"

Past, Present, and Future of Aerial Robotic Manipulators

Abstract: This article analyzes the evolution and current trends in aerial robotic manipulation, comprising helicopters, conventional underactuated multirotors, and multidirectional thrust platforms equipped with a wide variety of robotic manipulators capable of physically interacting with the environment. It also covers cooperative aerial manipulation and interconnected actuated multibody designs. The review is completed with developments in teleoperation, perception, and planning. Finally, a new generation of aerial robotic manipulators is presented with our vision of the future.

Design of multirotor aerial vehicles: A taxonomy based on input allocation:

Abstract: This paper reviews the effect of multirotor aerial vehicle designs on their abilities in terms of tasks and system properties. We propose a general taxonomy to characterize and describe multirotor ...

Physical Human-Robot Interaction With a Tethered Aerial Vehicle: Application to a Force-Based Human Guiding Problem

Abstract: Today, physical human-robot interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, aerial physical interaction is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach with a controller, inspired by the literature on flexible manipulators, that computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to nonidealities like model and tracking errors, additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI.

Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations

Abstract: In this paper, we present a model predictive controller for a fully actuated aerial manipulator to track a hybrid force and pose trajectory at the end-effector in an aerial interaction task. A force sensor at the end-effector is used to detect contact and to directly control the interaction force. We propose an approach for automatic transition between three operation modes which reflect the state of contact constraints, including free flight and two modes for force control based on static or dynamic friction at the end-effector. This division into three modes allows for different mode-specific controller tunings to optimize the desired performance throughout an interaction task. Results from flight experiments which combine force, position, and attitude tracking, show the performance of the controller in terms of accuracy and precision. The performance is further benchmarked against a hybrid force/impedance controller.

Dynamic End Effector Tracking With an Omnidirectional Parallel Aerial Manipulator

Abstract: To address the challenge of precise, dynamic and versatile aerial manipulation, we present an aerial manipulation platform consisting of a parallel 3-DOF manipulator mounted to an omnidirectional tilt-rotor aerial vehicle. The general modeling of a parallel manipulator on an omnidirectional floating base is presented, which motivates the optimization and detailed design of the aerial manipulator parameters and components. Inverse kinematic control of the manipulator is coupled to the omnidirectional base pose controller with a dynamic compensation term, going beyond common decoupled approaches. This presents a baseline for the control of redundant omnidirectional aerial manipulators. Experimental flights show the advantages of an active manipulator vs. a fixed arm for disturbance rejection and end effector tracking performance, as well as the practical limitations of the dynamic compensation term for fast end effector trajectories. The results motivate future studies for precise and dynamic aerial manipulation.

Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

Abstract: Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. We experimentally demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from non-informative trajectory by 13.3% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.

Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction

Abstract: An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time.

Understanding the omnidirectional capability of a generic multi-rotor aerial vehicle

Abstract: The aim of this work is to present the necessary conditions for the design of an omnidirectional Multi-Rotor Aerial Vehicle (MRAV), while taking into consideration its geometry, weight, and actuation limits. The work formally defines these conditions and presents numerical metrics that reflect the satisfaction of the omnidirectional property. These metrics are then applied to assess the omnidirectional property of “Omni-plus-seven ”, i.e., an omnidirectional MRAV consisting of a hepta-rotor with uni-directional thrusters [1]. Finally the work shows the use of such metrics in the design of a new platform with similar geometry and modified weight and actuators.

Simulation and Experimental Results

Abstract: In this chapter we shall present the results obtained by the experimental and numerical campaign, apt to validate the proposed control and estimation methods presented in Chap 4 In particular, we recall that we designed: two hierarchical controllers for the outputs \(\mathbf {y}^a\), \({\mathbf {y}^{b}}\); three dynamic feedback linearizing controllers for the output \(\mathbf {y}^a\), \({\mathbf {y}^{b}}\), and \(\mathbf {y}^c\); a nonlinear observer based on IMU and three encoders readings; a nonlinear observer based on the IMU readings only, valid for the reduced model

Coordinated Multi-Robot Trajectory Tracking over Sampled Communication.

Abstract: In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking {in a coordinated way} when the sampling time of communications is non-negligible, disrupting the theoretical convergence guarantees of standard control designs. Given a feasible desired trajectory in the configuration space, the proposed controller receives measurements from the system at sampled time instants and computes velocity references for the robots, which are tracked by a low-level controller. We propose a jointly designed feedback plus feedforward controller with provable stability and error convergence guarantees, and further show that the obtained controller is amenable of decentralized implementation. We test the proposed control strategy via numerical simulations in the scenario of cooperative aerial manipulation of a cable-suspended load using a realistic simulator (Fly-Crane). Finally, we compare our proposed decentralized controller with centralized approaches that adapt the feedback gain online through smart heuristics, and show that it achieves comparable performance.

Theory of Tethered Aerial Vehicles

Abstract: This chapter is the core of the theoretical investigation of tethered aerial vehicles In the first part, we provide an overview of the state of the art on the related topic Here we highlight the drawbacks and the gaps of the proposed methods We then define the objectives and thus the contribution of this book which aim at filling these gaps Our complete analysis starts deriving a generic model for tethered aerial vehicles, which also includes particular instances of the system Leveraging on this dynamic model, we investigate the differential flatness of the system, finding two different set of flat output These reveals new and interesting properties of tethered aerial vehicles with respect to standard vehicles in contact-free flight Exploiting this useful dynamic property, we derive two different types of controllers One is simple to implement and suitable for quasi-static maneuvers The other has been instead designed for the tracking of more dynamic trajectories Finally, in order to close the control loop with real sensors, we investigate the observability of the system’s state trying to find the minimal set of sensors that makes the state observable This analysis identifies two sensors setups, one for the 3D and one for the 2D cases In both cases, we derive an High Gain Observe to obtain an estimation of the state from the available measurements

Model of the Robotic Elements

Abstract: In Chap 2 we presented the fundamental methods employed for the analytical study of tethered aerial vehicles However, in order to practically apply the presented control and estimation methods to the real robotic platform, we need a good understanding of the underlying subsystems, such as actuators and sensors, and the corresponding mathematical models Therefore, in the following we shall closely analyze the robotic systems under exam, namely aerial vehicles connected by links, looking at their subsystems, actuators and sensors We firstly characterize a generic link and a generic unidirectional-thrust aerial vehicle in a free-flight condition, deriving their dynamic models Afterwards, looking at the robotic system from an actuation point of view, we closely analyze the thrusters, composed by brushless motor plus propeller, and servo/torque motors employed to actively change the link length Finally, this time looking at the robotic system from a sensing point of view, we review the standard sensory setup that one can find on aerial vehicles, and the additional sensors that we intend to use to measure the configuration of the link The following does not claim to be a deep and through discussion on aerial vehicles and their actuators and sensors On the contrary, the intent is to introduce the mathematical models of the subsystems composing a tethered aerial vehicle, in order to better understand the relative theoretical results For a more detailed discussion on aerial vehicles we will refer the reader to appropriate references

Modeling and Control of an Omnidirectional Micro Aerial Vehicle Equipped with a Soft Robotic Arm.

Abstract: Flying manipulators are aerial drones with attached rigid-bodied robotic arms and belong to the latest and most actively developed research areas in robotics. The rigid nature of these arms often lack compliance, flexibility, and smoothness in movement. This work proposes to use a soft-bodied robotic arm attached to an omnidirectional micro aerial vehicle (OMAV) to leverage the compliant and flexible behavior of the arm, while remaining maneuverable and dynamic thanks to the omnidirectional drone as the floating base. The unification of the arm with the drone poses challenges in the modeling and control of such a combined platform; these challenges are addressed with this work. We propose a unified model for the flying manipulator based on three modeling principles: the Piecewise Constant Curvature (PCC) and Augmented Rigid Body Model (ARBM) hypotheses for modeling soft continuum robots and a floating-base approach borrowed from the traditional rigid-body robotics literature. To demonstrate the validity and usefulness of this parametrisation, a hierarchical model-based feedback controller is implemented. The controller is verified and evaluated in simulation on various dynamical tasks, where the nullspace motions, disturbance recovery, and trajectory tracking capabilities of the platform are examined and validated. The soft flying manipulator platform could open new application fields in aerial construction, goods delivery, human assistance, maintenance, and warehouse automation.

Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots

Abstract: Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. In experiments we demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from non-informative trajectory by 13.3\% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.

Towards Multiple Tethered Aerial Vehicles

Abstract: Here we consider a multi-agent extension of the original problem analyzed in Chap. 4, by looking at a system composed by two underactuated flying vehicles lying on a vertical plane that are connected to the ground and to each other through two generic links, as depicted in Fig. 7.1. One can notice the similarity with a classic two-link Cartesian robot where the end of the chain represents the end-effector, while the aerial vehicles are the actuated joints of the robot. For this singular system, never studied before according to our best knowledge, we aim to extend part of the results found for the single tethered case. In particular, we want to control not only the elevation but also the internal force of the two links. Moreover we want to obtain the tracking of the output of interest along any desired time-varying trajectory, instead of just achieving regulation to constant values. For this goal we shall show that also in this case the elevations and internal force along the links are differential flat/feedback outputs. Following the analysis of Chap. 4, we will design a state feedback linearizing controller for the precise tracking of the output of interest. Finally, we investigate which is the minimal set of sensors needed to estimate the full state of the system. Based on such sensory setup we will design a nonlinear observer based on the HGO in order to obtain the sough estimation of the state. We remark that this topic is still a work in progress that will be further developed in the future.

Control of over-redundant cooperative manipulation via sampled communication.

Abstract: In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulating robots is redundant, i.e. a desired object configuration can be obtained by different configurations of the robots. The objective of this work is to show that communication can be used to implement cooperative local feedback controllers in the robots to improve disturbance rejection and reduce structural stress in the object. In particular we consider the realistic scenario where measurements are sampled and transmitted over wireless, and the sampling period is comparable with the system dynamics time constants. We first propose a kinematic model which is consistent with the overall systems dynamics under high-gain control and then we provide sufficient conditions for the exponential stability and monotonic decrease of the configuration error under different norms. Finally, we test the proposed controllers on the full dynamical systems showing the benefit of local communication.

Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction

Abstract: An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time.

Theory and Experiments for a Practical Usecase

Abstract: In many aerial robot applications such as search and rescue, the task consists on providing assistance in hostile environments such as mountains or civil areas after natural catastrophes In this scenarios it is very likely that the terrain is not flat, making the landing and takeoff maneuvers of the aerial robot very complicate and unsafe In contact-free conditions, the complexity of the task is increased by the underactuation of standard unidirectional-thrust aerial vehicle In this chapter we shall show that the use of physical interaction, and in this case exploiting the tether, a unidirectional-thrust aerial vehicle can perform the task in a much robust and reliable way In this chapter we will provide a formal study of the problem, proving the superiority of the tethered system We shall then how how the results of Chap 4 have been exploited to perform the task A simple but effective trajectory generator is also derived for the particular task Real experiments are presented validating the proposed method