Optimal Visual Servoing for differentially flat underactuated systems

Sheckells, Matthew; Garimella, Gowtham; Kobilarov, Marin

doi:10.1109/iros.2016.7759815

Cited by 23 publications

(25 citation statements)

References 30 publications

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“…The proposed optimization procedure leads to computational time constraints that do not allow to constantly re-optimize the whole path while following it, penalizing the vehicle to obtain even more better tracking performance. Our work builds on a part of the problem formulation given in [14], but our solution presents significant differences: (i) the whole problem is decoupled and split into two optimization problems; (ii) The formulation of the target re-projection error assumes a slightly different form, by enabling to scale the different error components; (iii) the NMPC explicitly takes into account two dynamic effects and the low-level controller that runs on the UAV. The coupling between perception and planning has also been addressed in [2], where an UAV has to simultaneously localize itself with respect to a gap and pass trough it.…”

Section: A Related Workmentioning

confidence: 99%

“…In all the above mentioned work, except for [14], there is no guarantee that the target is constantly kept in the camera field of view because they don't directly take into account the vehicle dynamics.…”

Section: A Related Workmentioning

confidence: 99%

“…4.d as π, the extrinsic parameters between the camera reference system C sensor and the body frame B, and the desired position of the target object in the image plane p i . Differently from [14], we make use of a weighting matrix H in place of a scalar weighting factor, allowing us to scale the different components of the reprojection error. Ideally, we want to have an H that penalizes mostly the error along the smaller dimension of the input image.…”

Section: B Optimal Trajectory Computationmentioning

confidence: 99%

See 2 more Smart Citations

Effective target aware visual navigation for UAVs

Potena

Nardi

Pretto

2017

2017 European Conference on Mobile Robots (ECMR)

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In this paper we propose an effective visionbased navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target reprojection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: B Optimal Trajectory Computationmentioning

confidence: 99%

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Effective target aware visual navigation for UAVs

Potena

Nardi

Pretto

2017

2017 European Conference on Mobile Robots (ECMR)

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“…In [15], a hybrid visual servoing technique for differentially flat systems was presented. A polynomial parameterization of the flat outputs of the system was required, and due to the computational load required by the designed optimization framework, an optimal trajectory was computed in advance and never replanned.…”

Section: B Related Workmentioning

confidence: 99%

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

Falanga

Foehn

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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146

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We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sensing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, together with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the conflict between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions. SUPPLEMENTARY MATERIAL Video: https://youtu.be/9vaj829vE18 Code: https://github.com/uzh-rpg/rpg_mpc * These authors contributed equally to this manuscript.

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“…UAV frames represent trajectory points along the prediction horizon T : the blue frames represent the refined portion of the lattice, while the red ones correspond to the coarser rest of trajectory. does not allow real-time computation, thus making NMPC impractical in real-world applications 1 [19] [21].…”

Section: Introductionmentioning

confidence: 99%

Non-linear model predictive control with adaptive time-mesh refinement

Potena

Corte

Nardi

et al. 2018

2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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In this paper, we present a novel solution for realtime, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code. * These two authors contribute equally to the work.

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Optimal Visual Servoing for differentially flat underactuated systems

Cited by 23 publications

References 30 publications

Effective target aware visual navigation for UAVs

Effective target aware visual navigation for UAVs

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

Non-linear model predictive control with adaptive time-mesh refinement

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