In this article, a macro–mini aerial manipulator with elastic suspension is introduced. The mini is an omnidirectional aerial manipulator suspended from the macro by a spring. The macro is a Cartesian robot that moves the anchoring point of the spring. This design combines the advantages of the large workspace of the macrorobot with the high dynamics of aerial vehicles, while reducing energy consumption thanks to gravity compensation. A partitioned control scheme is first implemented to regulate the aerial manipulator and its carrier separately. The redundancy resolution strategy positions the macrorobot to minimize the energy consumption of the aerial manipulator at steady state. Then, a nonlinear model predictive controller replaces the partitioned controller to improve further the efficiency of the combined system, notably by anticipating the slow dynamics of the macrorobot. A sufficient condition for offset-free tracking has been investigated theoretically. Experiments with a cable-driven parallel robot as macro are carried out to assess the added value of the carrier. Both controllers are validated and compared experimentally.

Aerial Manipulators with Elastic Suspension (AMES) may be seen as a hybrid robot mixing properties of classical Aerial Manipulators (AMs) and Cable-Driven Parallel Robots (CDPRs). The optimal design and control of an AMES using unidirectional thrusters are considered in this paper. To maximize the workspace, an optimization algorithm is proposed. The position and orientation of the thrusters are optimized by adapting methods borrowed from both the AM and CDPR communities. The resulting design is used to build a prototype. Preliminary experimentations are carried out to validate the theoretical workspace and assess the trajectory tracking performance of this AMES. Experiments highlight the significant improvements with respect to a previous suboptimal prototype.

Aerial manipulation increases significantly the workspace size of robotic manipulators. However, aerial manipulation suffers from a lack of autonomy due to limited embedded energy. The Aerial Manipulator with Elastic Suspension (AMES) is designed to cope with this issue. It is an omnidirectional aerial vehicle equipped with a gripper and suspended under a robotic carrier by a spring for gravity compensation. In this paper, the AMES is controlled with a nonlinear model predictive controller (NMPC). To eliminate the steady-state errors, an observer based on a model of the AMES augmented with constant disturbances is implemented in conjunction with the NMPC controller. Experiments illustrate the efficiency of the NMPC by comparing it to a computed torque controller.

Since omnidirectional aerial vehicles can generate a six degrees of freedom wrench, they could be used for dexterous manipulation tasks without the need for an additional robotic arm. However, they suffer from a reduced efficiency and dynamics range due to the huge amount of energy lost in gravity compensation.In this work, we introduce an omnidirectional aerial manipulator suspended from a cable-driven parallel robot (CDPR) by a spring, combining the advantages of the CDPR large workspace with the high dynamics of aerial vehicles, while reducing energy consumption thanks to gravity compensation.A partitioned control scheme is implemented to regulate both systems separately. A preliminary control strategy is proposed for the CDPR motion that minimizes the total energy consumption. Experiments are carried out to assess the added value of the CDPR carrier.

This paper presents a preliminary study of an Aerial Manipulator suspended by a spring to a robotic carrier. The suspended aerial manipulator is actuated by six pairs of contra-rotating propellers generating a 6-DoF wrench. Simulations show path following results using a computed torque (feedback linearization) control strategy. Active vibration canceling is validated experimentally on a first prototype.

This work studies redundant actuation for both trajectory tracking and disturbance rejection on flexible cable-driven parallel robots (CDPR). High dynamics/bandwidth unidirectional force generators, like air propellers, are used in combination with conventional but slower cable winding winches. To optimally balance the action of the two types of actuation within their saturation constraints, a model predictive controller is used. Experiments show the added value of on-board propulsion units with respect to winch-only control in order to improve the overall CDPR dynamic behavior.

This work is a preliminary study assessing the feasibility of using cold-gas thrusters for active damping of flexible cable-driven parallel robots. The concept is validated experimentally on a planar robot embedding custom-built supersonic air thrusters operating at an industry-standard pressure level.

In recent years, there have been significant efforts to standardize a routing protocol for Low-power and Lossy Networks (LLNs). This effort has culminated in standard IPv6 routing protocol for LLNs (RPL). The main interest of RPL is to improve routing in an LLN minimizing the usage of network resources. For this, RPL builds acyclic graphs and applies an Objective Function (OF) which is responsible of choosing the preferred parent and the best links during the construction of the Destination Oriented Directed Acyclic Graph (DODAG). This paper introduces a new OF, based on a Non-Linear Length (NL-OF) which takes into account any number of metrics and constraints for QoS routing. NL-OF ensures that each path in the DODAG respects the input constraints. The NL-OF can be used to meet the requirements of sensible applications, such as real-time applications. A significant part of this work aims at studying the theoretical aspect of the NL-OF. Finally, using Cooja simulator, we evaluate the performance of NL-OF. We show that our new Objective Function gives a good result and outperforms the three existing OFs when considering three QoS parameters which are end-to-end Delay, Packet Loss and Jitter.

Internet of Things is one of the most promising domain for developing new and smart applications. For some applications, Quality of Service requirement may be imposed. However, the things connected in this kind of networks are often vulnerable and dotted by limited energy (Low-power and Lossy Networks, LLNs). These networks need special solutions for routing and RPL is a standard IPv6 routing protocol to respond to needs. RPL build Destination Oriented Directed Acyclic Graphs (DODAGs) for routing. Recently, some propositions have been formulated to satisfy QoS requirements in LLNs using RPL even if there are several QoS constraints. A greedy DODAG construction has been proposed based on a Non-Linear Length which takes into account any number of constraints for QoS routing. However, it is known that the multi-constrained QoS routing is an NP-hard problem and its solution is not always a simple acyclic graph (not a tree). In this paper we present the computation of the exact solution in LLNs between a Border Router and the nodes respecting a set of QoS constraints. We propose the modification of RPL for this. Our analysis also covers a set of parameterized polynomial algorithms. We evaluate the performance of the proposed algorithms in term of execution time and scalability, the number of missing nodes during the construction of the DODAG and the quality of the paths from the Border Router to each node in the constructed DODAG.

The Surface Moisture and Ocean Salinity (SMOS) mission senses ocean salinity and soil moisture by measuring Earth's brightness temperature using interferometry in the L-band. These interferometry measurements known as visibilities constitute the SMOS L1A data product. Despite the L-band being reserved for Earth observation, the presence of illegal emitters causes radio frequency interference (RFI) that masks the energy radiated from the Earth and strongly corrupts the acquired images. Therefore, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this paper, we propose a variational model to recover superresolved, denoised brightness temperature maps by decomposing the images into two components: an image T that models the Earth's brightness temperature and an image O modeling the RFIs.

This paper proposes a simple solution for stabilization of a nano-hexacopter carrying a manipulator arm in order to increase the type of missions achievable by these types of systems. The manipulator arm is attached to the lower part of the hexacopter. The motion of the arm induces a change of the center of mass of the whole body, which induces torques which can produce the loss of stability. The present work deals with the stabilization of the whole system - that is hexacopter and arm - by means of a set of nonlinear control laws. First, an attitude control, stabilizes the hexacopter to a desired attitude taking into account the movement of the arm. Then, a suitable virtual control and the translational dynamics allow the formulation of a nonlinear controller, which drives the aerial vehicle to a desired position. Both controls consist in saturation functions. Experimental results validate the proposed control strategy and compares the results when the motion of the arm is taken into account or not.

In this paper, a state-feedback linear-quadratic regulator (LQR) is proposed for event-based control of a linear system. An interesting property of LQRs is that an optimal response of the system can be obtained in accordance to some specifications, like the actuator limits. An integral action is also added in order to not only restrict the study to null stabilization but also to tracking. The idea is to consider an external control loop and stabilize the integral of the error between the measurement and a desired setpoint to track. However, an event-triggered integral can lead to important overshoots when the interval between two successive events becomes large. Therefore, an exponential forgetting factor of the sampling interval is proposed as a solution to avoid such problems. The whole proposal is tested on a real-time system (a gyroscope) in order to highlight its ability, the reduction of control updates and the respect to the actuator limits.

Data degradation by radio frequency interferences (RFI) is one of the major challenges that SMOS and other interferometers radiometers missions have to face. Although a great number of the illegal emitters were turned off since the mission was launched, not all of the sources were completely removed. Moreover, the data obtained previously is already corrupted by these RFI. Thus, the recovery of brightness temperature from corrupted data by image restoration techniques is of major interest. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map based on two spatial components: an image u that models the brightness temperature and an image o modeling the RFI. The approach is totally new to our knowledge, in the sense that it is directly and exclusively based on the visibilities (L1a data), and thus can also be considered as an alternative to other brightness temperature recovery methods.

Estimates of soil moisture and surface salinity are of significant importance to improve meteorological and climate prediction. The SMOS mission monitor these quantities, by measuring the brightness temperature by means of L-band aperture synthesis interferometry. Despite the L-band being reserved for Earth and space exploration, SMOS images reveal large number of strong outliers, produced by illegal antennas emitting in this band. In this work we propose a variational approach to recover a super-resolved, denoised brightness temperature map. The measurements are modeled as the superposition of three super-resolved components in the spatial domain: the target brightness temperature map u, an image o modeling the outliers, and Gaussian noise n. This decomposition allows to isolate each of its constituent parts, thanks to a sparsity operator that acts on o, and a bounded variation prior on u that extrapolates its spectrum promoting a non-oscillating behavior. The proposed model is interesting in itself, as it is general enough to be applied to other restoration problems. Experiments on real and synthetic data confirm the suitability of the proposed approach.

Restoring a regular image from irregular samples was shown feasible via quadratic regularization using Fourier and spline representations. When the image is also blurred and noisy (as is usually the case in satellite imaging) ℓ1regularizers (like TV) were shown most effective, but their Fourier-domain implementation has a prohibitive computational cost. We present here a new method that combines a spline representation (for speed) with TV regularization to obtain a more accurate and good-quality restored image. Extending this approach to the blurred case is not as trivial as in the Fourier representation. Indeed, in order to avoid the sampling operator to lose its sparse structure, a projection of the convolution operator on a spline space becomes necessary. Extensive experimental results with automatic regularization and stopping criteria show that our method achieves the accuracy of with much less computational cost, closer to.

This paper introduces a family of non-adaptive geometrical wavelets that have high anisotropy and directionality. Unlike curvelets and contourlets, they have no redundancy and form orthonormal bases of L/sup 2/ (R/sup 2/). Their implementation derives from a single non-separable filter bank structure that is designed from 1-D and quincunx filters.

Coding systems based on block DCT, such as the JPEG standard, are known to produce blocking and Gibbs effects. We propose a method to remove these artifacts without smoothing images and without loosing their perceptual features. It consists of a weighted total variation minimization constrained by the knowledge of quantization intervals. A fast algorithm is proposed and experiments suggest better performance than state-of-the-art deblocking algorithms.

Recent years have seen the development of signal denoising algorithms based on the wavelet transform. It has been shown that thresholding the wavelet coefficients of a noisy signal allows us to restore the smoothness of the original signal. However, wavelet denoising suffers a main drawback: around discontinuities the reconstructed signal is smoothed, exhibiting the pseudo-Gibbs phenomenon. We consider the problem of denoising piecewise smooth signals with sharp discontinuities. We propose to apply a traditional wavelet denoising method and to restore the denoised signal using a total variation minimization approach. This second step allows us to remove the Gibbs phenomena and therefore to restore sharp discontinuities, while the other structures are preserved. The main innovation of our algorithm is to constrain the total variation minimization by the knowledge of the remaining wavelet coefficients. In this way, we make sure that the restoration process does not deteriorate the information that has been considered as significant in the denoising step. With this approach we substantially improve the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts.