Showing papers by "Marco Tognon published in 2022"

Energy Tank-Based Policies for Robust Aerial Physical Interaction with Moving Objects

Abstract: Although manipulation capabilities of aerial robots greatly improved in the last decade, only few works addressed the problem of aerial physical interaction with dynamic environments, proposing strongly model-based approaches. However, in real scenarios, modeling the environment with high accuracy is often impossible. In this work, we aim at developing a control framework for Omnidirectional Micro Aerial Vehicles (OMAVs) for reliable physical interaction tasks with articulated and movable objects in the presence of possibly unforeseen disturbances, and without relying on an accurate model of the environment. Inspired by previous applications of energy-based controllers for physical interaction, we propose a passivity-based impedance and wrench tracking controller in combination with a momentum-based wrench estimator. This is combined with an energytank framework to guarantee the stability of the system, while energy and power flow-based adaptation policies are deployed to enable safe interaction with any type of passive environment. The control framework provides formal guarantees of stability, which is validated in practice considering the challenging task of pushing a cart of unknown mass, moving on a surface of unknown friction, as well as subjected to unknown disturbances. For this scenario, we present, evaluate and discuss three different policies.

Precise Cable-Suspended Pick-and-Place with an Aerial Multi-robot System

Abstract: Abstract This work introduces the G-Fly-Crane, a proof-of-concept aerial multi-robot system designed to demonstrate the advantage of using multiple aerial robots as a valuable tool for novel construction techniques, not requiring the use of heavy engines and costly infrastructures. We experimentally demonstrate its capability to perform pick-and-place and manipulation tasks in a construction scenario, with an increased payload capacity and dexterity compared to the single robot case. The system is composed of three aerial robots connected to a platform by three pairs of cables. The platform is equipped with a gripper, enabling the grasping of objects. The paper describes in detail the hardware and software architecture of our prototype and explains the implemented control methods. A shared control strategy incorporates the human operator in the control loop, thus increasing the overall system reliability when performing complex tasks. The paper also discusses the next steps required to bring this technology from indoor laboratory conditions to real-world applications.

Adaptive Tank-based Control for Aerial Physical Interaction with Uncertain Dynamic Environments Using Energy-Task Estimation

Abstract: While aerial manipulation has witnessed noticeable growth as a field in the last decade, most works investigated forms of interaction with static and rigid environments only. Whenever dynamic environments were considered, the employed methods often relied on the knowledge of the model of the environment, which in most real applications cannot be obtained. In this work, we propose an adaptive controller for a fully actuated UAV performing stable and efficient physical interaction tasks with unmodeled and dynamic objects moving in unknown environments. We develop a passive time-varying impedance controller and wrench tracking controller, whose adaptable parameters allow us to minimize tracking error and instabilities during the execution of the interaction task. Robust stability is guaranteed by energy tanks, with the addition of a task-based formulation for adapting online the tank parameters in order to always provide the system with an adequate amount of energy. The control framework is validated both in simulations and experimentally by interacting with an unmodeled cart moving in passive time-varying environments, while subjected to unknown disturbances.

Human-State-Aware Controller for a Tethered Aerial Robot Guiding a Human by Physical Interaction

Abstract: With the rapid development of Aerial Physical Interaction, the possibility to have aerial robots physically interacting with humans is attracting a growing interest. In one of our previous works [1], we considered one of the first systems in which a human is physically connected to an aerial vehicle by a cable. There, we developed a compliant controller that allows the robot to pull the human toward a desired position using forces only as an indirect communication-channel. However, this controller is based on the robot-state only, which makes the system not adaptable to the human behavior, and in particular to their walking speed. This reduces the effectiveness and comfort of the guidance when the human is still far from the desired point. In this paper, we formally analyze the problem and propose a human-state-aware controller that includes a human’s velocity feedback. We theoretically prove and experimentally show that this method provides a more consistent guiding force which enhances the guiding experience.

Aerial Layouting: Design and Control of a Compliant and Actuated End-Effector for Precise In-flight Marking on Ceilings

Abstract: —Aerial robots have demonstrated impressive feats of precise control, such as dynamic flight through openings or highly complex choreographies. Despite the accuracy needed for these tasks, there are problems that require levels of precision that are challenging to achieve today. One such problem is aerial interaction. Advances in aerial robot design and control have made such contact-based tasks possible and opened up research into challenging real-world tasks, including contact-based inspection. However, while centimetre accuracy is sufficient and achievable for inspection tasks, the positioning accuracy needed for other problems, such as layouting on construction sites or general push-and-slide tasks, is millimetres. To achieve such a high precision, we propose a new aerial system composed of an aerial vehicle equipped with a novel “smart” end-effector leveraging a stability-optimized Gough-Stewart mechanism. We present its design process and features incorporating the princi-ples of compliance, multiple contact points, actuation, and self- containment. In experiments, we verify that the design choices made for our novel end-effector are necessary to obtain the desired positioning precision. Furthermore, we demonstrate that we can reliably mark lines on ceilings with millimetre accuracy without the need for precise modeling or sophisticated control of the aerial robot.

A Planning-and-Control Framework for Aerial Manipulation of Articulated Objects

Abstract: While the variety of applications for Aerial Manipulators (AMs) has increased over the last years, they are mostly limited to push-and-slide tasks. More complex manipulations of dynamic environments are poorly addressed and still require handcrafted designs of hardware, control, and trajectory planning. In this letter we focus on the active manipulation of articulated objects with AMs. We present a novel planning and control approach that allows the AM to execute complex interaction maneuvers with as little as possible priors given by the operator. Our framework combines sampling-based predictive control to generate pose trajectories with an impedance controller for compliant behaviours, applied to a fully-actuated flying platform. The framework leverages a physics engine to simulate the dynamics of the platform and the environment in order to find optimal motions to execute manipulation tasks. Experiments on two selected examples of pulling open a door and of turning a valve show the feasibility of the proposed approach.

Indirect Force Control of a Cable-Suspended Aerial Multi-Robot Manipulator

Abstract: We present the control in physical interaction with the environment of a Cable-suspended Aerial Multi-Robot Manipulator (CS-AMRM) called the Fly-Crane, composed of three aerial vehicles towed to a platform by means of six cables. The control strategy enables the system to accurately and safely perform tasks involving expected or unexpected interactions between the platform and the environment, in the absence of dedicated force/torque sensors. A previously developed Inverse Kinematic Controller (IKC) is enhanced with an admittance framework, and contacts are estimated through a generalized momentum-based observer. To assess the validity of our approach, and to provide practical insights into the method, we perform extensive experimental tests, comprehending the admittance property shaping to modulate stiffness, damping, and virtual mass, as well as experiments in a more realistic scenario involving contacts between the Fly-Crane and the environment.

Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents

Abstract: As the performance of autonomous systems increases, safety concerns arise, especially when operating in non-structured environments. To deal with these concerns, this work presents a safety layer for mechanical systems that detects and responds to unstable dynamics caused by external disturbances. The safety layer is implemented independently and on top of already present nominal controllers, like pose or wrench tracking, and limits power flow when the system’s response would lead to instability. This approach is based on the computation of the Largest Lyapunov Exponent (LLE) of the system’s error dynamics, which represent a measure of the dynamics’ divergence or convergence rate. By actively computing this metric, divergent and possibly dangerous system behaviors can be promptly detected. The LLE is then used in combination with Control Barrier Functions (CBFs) to impose power limit constraints on a jerk controlled system. The proposed architecture is experimentally validated on an Omnidirectional Micro Aerial Vehicle (OMAV) both in free flight and interaction tasks.

Towards 6DoF Bilateral Teleoperation of an Omnidirectional Aerial Vehicle for Aerial Physical Interaction

Abstract: Bilateral teleoperation offers an intriguing solution towards shared autonomy with aerial vehicles in contact-based inspection and manipulation tasks. Omnidirectional aerial robots allow for full pose operations, making them particularly attractive in such tasks. Naturally, the question arises whether standard bilateral teleoperation methodologies are suitable for use with these vehicles. In this work, a fully decoupled 6DoF bilateral teleoperation framework for aerial physical interaction is designed and tested for the first time. The method is based on the well established rate control, recentering and interaction force feedback policy. However, practical experiments evince the difficulty of performing de-coupled motions in a single axis only. As such, this work shows that the trivial extension of standard methods is insufficient for omnidirectional teleoperation, due to the operator's physical inability to properly decouple all input DoFs. This suggests that further studies on enhanced haptic feedback are necessary.

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing

Abstract: The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a solution that senses, adapts, and remains robust against these environmental uncertainties remains an open challenge. This letter presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a neural network policy based on proprioceptive and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tilt-arm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. We attribute the success of the sim-to-real transfer to the inclusion of feedback control in the training and deployment. We achieved tracking performance and disturbance rejection that cannot be achieved using fine-tuned state of the art interaction control method.

MPC with Learned Residual Dynamics with Application on Omnidirectional MAVs

Abstract: —The growing field of aerial manipulation often relies on fully actuated or omnidirectional micro aerial vehicles (OMAVs) which can apply arbitrary forces and torques while in contact with the environment. Control methods are usually based on model-free approaches, separating a high-level wrench controller from an actuator allocation. If necessary, disturbances are rejected by online disturbance observers. However, while being general, this approach often produces sub-optimal control commands and cannot incorporate constraints given by the platform design. We present two model-based approaches to control OMAVs for the task of trajectory tracking while rejecting disturbances. The first one optimizes wrench commands and compensates model errors by a model learned from experimental data. The second one optimizes low-level actuator commands, allowing to exploit an allocation nullspace and to consider constraints given by the actuator hardware. The efficacy and real-time feasibility of both approaches is shown and evaluated in real-world experiments.

Reactive Motion Planning for Rope Manipulation and Collision Avoidance using Aerial Robots

Abstract: In this work we address the challenging problem of manipulating a flexible link, like a rope, with an aerial robot. Inspired by spraying tasks in construction and maintenance scenarios, we consider the case in which an autonomous end-effector (e.g., a spray nozzle moved by a robot or a human operator) is connected to a fixed point by a rope (e.g., a hose). To avoid collisions between the rope and the environment while the end-effector moves, we propose the use of an aerial robot as a flying companion to properly manipulate the rope away from collisions. The aerial robot is attached to the rope between the end-effector and the fixed point. Assuming no direct control of the end-effector (e.g., when operated by a human), we design a reactive and fast motion planner for the aerial robot. Grounding on the theory of Forced Geometric Fabrics, we design a motion planner that generates trajectories to drive the aerial robot to follow the end-effector, while manipulating the rope to avoid collisions in cluttered environments. To include the complex behavior of the flexible link, we propose a rope model that estimates its real-time state under forces and position-based interactions, as well as collisions with obstacle surfaces. Finally, we evaluate the system behavior and the motion planner performance in simulations, as well as in real-world experiments on an original spray painting application.