Energy Tank-Based Wrench/Impedance Control of a Fully-Actuated Hexarotor: A Geometric Port-Hamiltonian Approach

IEEE Robot. Autom. Lett.

Califano

2019

Self Cite

In this work, we approach the control problem of fully-actuated UAVs in a geometric port-Hamiltonian framework. The UAV is modeled as a floating rigid body on the special Euclidean group SE(3). A unified near-hovering motion and impedance controller is derived by the energy-balancing passivity-based control technique. A detailed analysis of the closed-loop system's behavior is presented for both the freeflight stability and contact stability of the UAV. The robustness of the control system to uncertainties is validated by several experiments, in which the UAV is controlled near its actuator limits. The experiments show the ability of the UAV to hover at its maximum allowed roll angle and apply its maximum allowed normal force to a surface, without the input saturation destabilizing the system.

Section: Introductionmentioning

confidence: 99%

Port-Hamiltonian Passivity-Based Control on SE(3) of a Fully Actuated UAV for Aerial Physical Interaction Near-Hovering

IEEE Robot. Autom. Lett.

Califano

2019

Self Cite

“…This alternative view of dynamical systems have proven very effective for modeling, analysis and control purposes within the framework of port-Hamiltonian systems. The interested reader in this topic can refer to [8,22,27].…”

Section: Discussionmentioning

confidence: 99%

“…By substituting equations (22,25,26) into ( 24) and expressing all quantities related to the child body in {p} using (15)(16)(17)(18), one can express the wrench W p,p c applied by the child on the parent as…”

Section: Parent-child Relationsmentioning

confidence: 99%

A Geometric Formulation of Multirotor Aerial Vehicle Dynamics

Hong

Noh

et al. 2021

Preprint

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A geometric dynamic modeling framework for generic multirotor aerial vehicles (MAV), based on a modern Lie group formulation of classical screw theory, is presented. Our framework allows for a broad range of rotor-wing con gurations: any number of rotors can be attached in arbitrary con gurations to either the body or wings, with the rotors and wings also tiltable. Our framework takes into account all masses and inertias of the MAV body and rotors, and accounts for both rotor thrust forces and moments as well as external aerodynamic and other forces. Compared to existing methods, our Lie group framework possesses several practical advantages useful for applications ranging from design optimization to model identi cation and trajectory optimization: (i) the dynamic equations can be easily transformed to coordinates of any reference frame; (ii) kinematic and mass-inertial parameters can be easily factored from the dynamic equations; (iii) exact, closedform analytic derivatives of the dynamics with respect to the con guration variables are easily derived. We demonstrate our systematic modeling procedure on examples of xed-tilt, variable-tilt, and hybrid MAVs with wings.

“…The simulations done in this work are centered around a fully-actuated hexarotor UAV [12] used for a task involving physical interaction with the environment. Simulations will be done for a surface sliding task.…”

Section: Simulation Setupmentioning

confidence: 99%

“…The simulation environment used consisted of a simulated hexarotor UAV equipped with an impedance controller (details can be found in [12]). The simulation environment is built over ROS (Robotics Operating System) via Gazebo and RotorS simulators [13].…”

Section: Implementation Detailsmentioning

confidence: 99%

Bayesian-Optimized Impedance Control of an Aerial Robot for Safe Physical Interaction with the Environment

Khattab

2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

Engelen

2019

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Impedance control is a widely used interactioncontrol technique for aerial and ground robots. To achieve consistent performance during impedance control tasks, an a-priori knowledge of the environment parameters is needed to adjust the controller's impedance parameters accordingly. Concentrating on tasks requiring constant impedance parameters throughout operation, a model-free learning framework is proposed to autonomously find the suitable parameters values. The framework relies on Bayesian optimization and episodic reward calculation requiring the drone to repeatedly perform a predetermined task in the environment actively searching in the impedance parameters space. The sampleefficiency and safety of learning were improved by adding two novel modifications to standard Bayesian optimization. The proposed technique was validated in a high fidelity simulation environment. The results show that the proposed framework is able to automatically find suitable impedance parameters values in different situations given the same initial knowledge and that the learned parameters values can be generalized to similar interaction tasks.