Task priority control for aerial manipulation

Santamaria-Navarro, Àngel; Lippiello, Vincenzo; Andrade‐Cetto, Juan

doi:10.1109/ssrr.2014.7017672

Cited by 26 publications

(22 citation statements)

References 22 publications

(25 reference statements)

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“…The overall estimation system acts as an odometer that provides absolute altitude, velocity, orientation, angular rate, and acceleration, with respect to a precise gravity reference, at a rate of 100 Hz. The x and y positions and the yaw angle are not observable, and their output is the result of an incremental estimation subject to drift -these modes can be observed with a lower update rate by a higher level task, such as a visual servoing (Santamaria-Navarro et al, 2014Rossi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

et al. 2017

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The combination of visual and inertial sensors for state estimation has recently found wide echo in the robotics community, especially in the aerial robotics field, due to the lightweight and complementary characteristics of the sensors data. However, most state estimation systems based on visual-inertial sensing suffer from severe processor requirements, which in many cases make them impractical. In this paper, we propose a simple, low-cost and high rate method for state estimation enabling autonomous flight of Micro Aerial Vehicles (MAVs), which presents a low computational burden. The proposed state estimator fuses observations from an inertial measurement unit, an optical flow smart camera and a time-of-flight range sensor. The smart camera provides optical flow measurements up to a rate of 200 Hz, avoiding the computational bottleneck to the main processor produced by all image processing requirements. To the best of our knowledge, this is the first example of extending the use of these smart cameras from hovering-like motions to odometry estimation, producing estimates that are usable during flight times of several minutes. In order to validate and defend the simplest algorithmic solution, we investigate the performances of two Kalman filters, in the extended and error-state flavors, alongside with a large number of algorithm modifications defended in earlier literature on visual-inertial odometry, showing that their impact on filter performance is minimal. To close the control loop, a non-linear controller operating in the special Euclidean group SE(3) is able to drive, based on the estimated vehicle's state, a quadrotor platform in 3D space guaranteeing the asymptotic stability of 3D position and heading. All the estimation and control tasks are solved on board and in real time on a limited computational unit.The proposed approach is validated through simulations and experimental results, which include comparisons with ground-truth data provided by a motion capture system. For the benefit of the community, we make the source code public.

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Section: Introductionmentioning

confidence: 99%

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

et al. 2017

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“…In these works, as in most of recent approaches (e.g., [16], [17], [18]) the problem is solved using hierarchical task composition control but in almost all cases without using optimal control. A close approach is [19], which presents a trajectory generation method for UAMs through quadratic programming, taking advantage of redundancy.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model predictive control for aerial manipulation

Lunni

Santamaria-Navarro

Rossi

et al. 2017

2017 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.

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“…Also promising applications are appearing using Unmanned Aerial Manipulators (UAM), i.e. UAVs equipped with robotic arms, to perform some manipulation operations [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Planar PØP: Feature-less pose estimation with applications in UAV localization

Amor-Martinez

Santamaria-Navarro

Herrero

et al. 2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Abstract-We present a featureless pose estimation method that, in contrast to current Perspective-n-Point (PnP) approaches, it does not require n point correspondences to obtain the camera pose, allowing for pose estimation from natural shapes that do not necessarily have distinguished features like corners or intersecting edges. Instead of using n correspondences (e.g. extracted with a feature detector) we will use the raw polygonal representation of the observed shape and directly estimate the pose in the pose-space of the camera. This method compared with a general PnP method, does not require n point correspondences neither a priori knowledge of the object model (except the scale), which is registered with a picture taken from a known robot pose. Moreover, we achieve higher precision because all the information of the shape contour is used to minimize the area between the projected and the observed shape contours. To emphasize the non-use of n point correspondences between the projected template and observed contour shape, we call the method Planar P∅P. The method is shown both in simulation and in a real application consisting on a UAV localization where comparisons with a precise ground-truth are provided.

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Task priority control for aerial manipulation

Cited by 26 publications

References 22 publications

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

Autonomous navigation of micro aerial vehicles using high-rate and low-cost sensors

Nonlinear model predictive control for aerial manipulation

Planar PØP: Feature-less pose estimation with applications in UAV localization

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