This paper presents a methodology for height control of a quadrotor that transports a constant unknown load, given the estimates on both weight and state variables, based on measurements from motion sensors installed on-board. The proposed control and estimation framework is a Multi-Model Adaptive Controller using LQR with integrative action and Kalman filter with integrative component. The control system obtained is validated both in simulation and experimentally, resorting to an off-the-shelf commercially available quadrotor equipped with an IMU, an ultrasound height sensor, and a barometer, among other sensors.

Animal locomotion exhibits all the features of complex non linear systems such as multi-stability, critical fluctuation, limit cycle behavior and chaos. Studying these aspects on real robots has been proved difficult and therefore results mostly rely on the use of computer simulation. Simple control approaches - based on phase oscillators - have been proposed and exhibit several of these features. In this work, we compare two types of controllers: (a) an open loop control approach based on phase oscillators and (b) the Tegotae-based closed loop extension of this controller. The first controller has been shown to exhibit synchronization features between the body and the controller when applied to a quadruped robot with compliant leg structures. In this contribution, we apply both controllers to the locomotion of a stiff quadruped structure. We show that the Tegotae-controller exhibits self-organizing behavior, such as spontaneous gait transition and critical fluctuation. Moreover, it exhibits features such as the ability to stabilize both asymmetric and symmetric morphological changes, despite the lack of compliance in the leg.

This work addresses the video synchronization problem, which consists in finding the temporal alignment between sequences of images acquired with unsynchronized cameras. This problem has been addressed before, under the assumptions that the cameras are static or jointly moving and that there are correspondences between the features visible in the different sequences. There are some methods in the literature that managed to drop one of these assumptions, but none of them was successful in getting rid of both assumptions simultaneously. In this work, we introduce a new strategy that synchronizes cameras that are allowed to move freely even when there are no correspondences between the features that are visible in the different sequences. Our approach consists in tracking features on two rigid objects that move independently on the scene and use the relative motion between them as a clue for the synchronization. New synchronization algorithms for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object are also presented. Even though the emphasis of this work is essentially on the theoretical contribution of the proposed methods, rather than on an exhaustive experimental validation, several proof of concept experiments conducted with both real and synthetic data are presented. In the case of static or jointly moving cameras, comparisons with a state-of-the-art approach are also provided.

This paper presents an on-line three dimensional trajectory planner for an unmanned vehicle following a Leader vehicle, where the Follower remains at a specified relative position with respect to the Leader. The planner defines the trajectory for a virtual vehicle that will be used as reference for the real Follower vehicle. The virtual Follower behaves like a three dimensional trailer attached to the Leader and its reference frame is used to specify the relative position vector. At the kinematic level, the proposed planner requires only the knowledge of the Leader's linear speed, but not its angular speed, and the Follower's trajectory can be generated on-line, in the sense that no a priori knowledge of the Leader's trajectory is required. Experimental results obtained with quadrotor vehicles are presented, which demonstrate the richness of the planned trajectories.

This paper presents a localization solution for the study of tagged marine animals (sources) based on direction and Doppler measurements. The source localization, in 3-D, is supported by a Portable Underwater Robotic Tool (PURT), comprising a Ultra-Short Baseline (USBL) aided INS navigation system, carried by a diver (agent). Furthermore, a Surface Robotic Tool (SRT) provides the agent with its absolute position expressed in inertial coordinates. The observability of the system is studied and realistic simulation results are presented, in the presence of measurement noise.

This work considers the problem of locating a single source from noisy range measurements to a set of nodes in a wireless sensor network. We propose two new techniques that we designate as Source Localization with Nuclear Norm (SLNN) and Source Localization with l1-norm (SL-l1), which extend to arbitrary real dimensions our prior work on 2D source localization formulated in the complex plane. Our approach is based on formulating a Maximum-Likelihood (ML) estimation problem, and then using convex relaxation techniques to obtain a semidefinite program (SDP) that can be globally and efficiently solved. SLNN directly approximates the Gaussian ML solution, and the relaxation is shown to be tighter than in other methods in the same class. We present an analysis of the convexity properties of the constraint set for the 2D complex version of SLNN (SLCP) to justify the observed tightness of the relaxation. We propose the SL-l1algorithm to address the Laplacian noise case, which models the presence of outliers in range measurements. We overcome the non-differentiability of the Laplacian likelihood function by rewriting the ML problem as an exact weighted version of the Gaussian case. In terms of accuracy of localization, the proposed algorithms globally outperform state-of-the-art optimization-based methods in different noise scenarios, while exhibiting moderate computational complexity.

This work aims to estimate multiple node positions in the presence of unknown turn-around times within the context of cooperative sensor network localization. In the adopted scheme, each target can communicate with a set of anchors (probably not in sufficient numbers) and a set of other tar- gets. Two-Way Times-of-Arrival between them are measured, which includes unknown processing delays at both channel endpoints. Since finding the Maximum Likelihood Estimates (MLE) of the positions and turn-around times given those measurements poses a difficult nonconvex optimization prob- lem, it is approximated by a Nonlinear Least Squares prob- lem. Then, the positions and turn-around times of multiple targets are estimated jointly by solving an Euclidean Distance Matrix completion problem. Simulations show that the local- ization accuracy of the proposed method is good, providing an initial point that subsequently enables MLE to attain the Cramér-Rao Lower Bound for all considered scenarios.

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM), with application to unmanned aerial vehicles. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the underlying system structure can be regarded as linear time varying for observability analysis and filter design purposes, from which a linear Kalman filter with GAS error dynamics follows naturally. The performance and consistency validation of the proposed sensor-based SLAM filter are successfully assessed with real data, acquired indoors, using an instrumented quadrotor.

This paper proposes novel cooperative navigation solutions for an Intervention Autonomous Underwater Vehicle (I-AUV) working in tandem with an Autonomous Surface Craft (ASC). The I-AUV is assumed to be moving in the presence of constant unknown ocean currents, and aims to estimate its position relying on measurements of its range to the ASC and of its depth relatively to the sea level. Two different scenarios are considered: in one, the ASC transmits its position and velocity to the I-AUV, while in the other the ASC transmits only its position, and the I-AUV has access to measurements of its velocity relative to the ASC. A sufficient condition for observability and a method for designing state observers with Globally Asymptotically Stable (GAS) error dynamics are presented for both problems. Finally, simulation results are included and discussed to assess the performance of the proposed solutions in the presence of measurement noise.

In this paper a new PCA-based positioning sensor and localization system for mobile robots to operate in unstructured environments (e.g. industry, services, domestic...) is proposed and experimentally validated. The inexpensive positioning system resorts to principal component analysis (PCA) of images acquired by a video camera installed onboard, looking upwards to the ceiling. This solution has the advantage of avoiding the need of selecting and extracting features. The principal components of the acquired images are compared with previously registered images, stored in a reduced onboard image database, and the position measured is fused with odometry data. The optimal estimates of position and slippage are provided by Kalman filters, with global stable error dynamics. The experimental validation reported in this work focuses on the results of a set of experiments carried out in a real environment, where the robot travels along a lawn-mower trajectory. A small position error estimate with bounded co-variance was always observed, for arbitrarily long experiments, and slippage was estimated accurately in real time.

Maneuvering vessel detection and tracking (VDT), incorporated with state estimation and trajectory prediction, are important tasks for vessel navigational systems (VNSs), as well as vessel traffic monitoring and information systems (VTMISs) to improve maritime safety and security in ocean navigation. Although conventional VNSs and VTMISs are equipped with maritime surveillance systems for the same purpose, intelligent capabilities for vessel detection, tracking, state estimation, and navigational trajectory prediction are underdeveloped. Therefore, the integration of intelligent features into VTMISs is proposed in this paper. The first part of this paper is focused on detecting and tracking of a multiple-vessel situation. An artificial neural network (ANN) is proposed as the mechanism for detecting and tracking multiple vessels. In the second part of this paper, vessel state estimation and navigational trajectory prediction of a single-vessel situation are considered. An extended Kalman filter (EKF) is proposed for the estimation of vessel states and further used for the prediction of vessel trajectories. Finally, the proposed VTMIS is simulated, and successful simulation results are presented in this paper.

In this paper, new methodologies for the estimation of the depth of a target with unknown dimensions, based on depth from focus strategies, are proposed. The image-based measurements are detailed, through the minimization of a new functional, deeply rooted on optical characteristics of the lens system, namely the point spread function. This work complements an inexpensive single pan and tilt camera-based indoor positioning and tracking system, resorting to complementary filters for depth estimation. A motivation example is provided, where the target dimensions are assumed as known. Then, an extension corresponding to an higher-order filter is presented, that tackles the problem at hand. To assess the performance of the proposed system, a series of indoor experimental tests for a range of operation of up to ten meter were carried out. A centimetric accuracy was obtained under realistic conditions.

This paper proposes an estimation algorithm for the determination of attitude installation matrix for laser detection and ranging systems (LADAR) mounted onboard autonomous vehicles, without requiring any prior knowledge on the terrain where the calibration mission is performed. The use of autonomous vehicles equipped with LADAR systems to conduct fully automatic surveys of terrain, infrastructures, or just to navigate safely in unknown environments, motivates the research on increasingly precise LADAR data acquisition and processing algorithms, for which the determination of the correct installation matrix is critical. The proposed methods rely on the minimization of the errors between several acquired data sets, by comparing each measured data set and a surface representation of the others. This error functional is minimized resorting to optimization tools for Riemannian manifolds enabling direct estimation of the installation matrix on the group of special orthogonal matrices SO(3). The proposed technique is extensively tested using simulated LADAR data sets under realistic acquisition conditions.

This paper presents an experimentally evaluated solution to the problem of estimating the attitude of a rigid body using rate gyros and a pan-tilt camera. A nonlinear attitude observer combines angular velocity measurements obtained from rate gyros with images of a planar scene provided by the camera. By directly exploiting the sensor information, a stabilizing feedback law is introduced, and exponential convergence to the origin of the estimation errors is shown. Additionally, an active-vision system is proposed that relies on an image-based exponentially input-to-state-stable control law for the pan and tilt angular rates of the camera to keep the features in the image plane. Using recent results in geometric numerical integration, a multirate implementation of the observer is proposed, which exploits the complementary bandwidth of the sensors. Practical considerations, such as the lens-distortion compensation and the computation of suitable observer feedback gains, are considered. Experimental results obtained with a high-accuracy motion rate table demonstrate the high level of performance attained by the proposed solution.

This paper addresses the problem of locating a single source from noisy range measurements in wireless sensor networks. An approximate solution to the maximum likelihood location estimation problem is proposed, by redefining the problem in the complex plane and relaxing the minimization problem into semidefinite programming form. Existing methods solve the source localization problem either by minimizing the maximum likelihood function iteratively or exploiting other semidefinite programming relaxations. In addition, using squared range measurements, exact and approximate least squares solutions can be calculated. Our relaxation for source localization in the complex plane (SLCP) is motivated by the near-convexity of the objective function and constraints in the complex formulation of the original (non-relaxed) problem. Simulation results indicate that the SLCP algorithm outperforms existing methods in terms of accuracy, particularly in the presence of outliers and when the number of anchors is larger than three.

This paper presents the design and performance evaluation of a low-cost Attitude and Heading Reference System (AHRS) for autonomous vehicles. A single sensor pack, an Inertial Measurement Unit (IMU), provides all the data required to feed the attitude filter. The design is sensor-driven and departs from traditional solutions as no explicit representations of the attitude, e.g., Euler angles, quaternions, or rotation matrices, are considered in the filter design. Moreover, the proposed solution includes the estimation of rate gyros biases, systematic tuning procedures, and also allows for the inclusion of frequency weights to model colored noise on the different sensor channels. Due to its inherent structure, the filter is complementary, allows for temporary loss of sensor measurements, and also copes well with slowly time-varying rate gyros biases. The performance of the proposed algorithm is experimentally evaluated with a low-cost IMU and resorting to a high precision calibration table, which provides ground truth signals for comparison with the resulting filter estimates.

This paper addresses the design, development, and test of an integrated Ultra Short Baseline (USBL) and Inertial Navigation System (INS) to be used as a low cost navigation system for underwater robotic vehicles. An architecture for the open prototype is proposed, the acoustic array design and calibration is discussed, the inertial sensors package is presented, supporting acoustic signals to be used are briefly enumerated, and implementation issues are detailed. The system includes also as a by-product the design of a transponder that replies to the interrogations sent out by the integrated USBL/INS system. Preliminary sea tests, conducted in an harbor, are presented to assess the feasibility of the acoustic positioning system, using namely Direct Sequence Spread Spectrum (DSSS) coded signals.

A new sensor-based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in three dimensions, using the information provided by an ultrashort baseline (USBL) positioning system. The guidance and control law is first derived using quaternions to express the vehicle's attitude kinematics, which are directly obtained from the time differences of arrival (TDOA) measured by the USBL sensor. The dynamics are then included resorting to backstepping techniques. The proposed Lyapunov-based control law yields global asymptotic stability in the absence of external disturbances and is further extended, keeping the same properties, to the case where constant known ocean currents affect the dynamics of the vehicle. Finally, a globally exponentially stable nonlinear TDOA and range-based observer is introduced to estimate the ocean current and uniform asymptotic stability is obtained for the overall closed-loop system. Simulations are presented illustrating the performance of the proposed solutions.

This paper proposes two estimation algorithms for the determination of attitude installation matrix for Laser Detection and Ranging systems (LADAR) mounted onboard autonomous vehicles. The use of autonomous vehicles equipped with LADAR systems to conduct fully automatic surveys of terrain, infrastructures, or just to navigate safely in unknown environments, motivates the research on increasingly precise LADAR data acquisition and processing algorithms, for which the determination of the correct installation matrix is critical. The proposed methods rely on the minimization of the errors between the measured data set and a representation of the real calibration surface. To minimize this error, two nonlinear optimization techniques are proposed, one that estimates the ZYX Euler angles and a second that uses optimization tools for Riemannian manifolds enabling direct estimation of the installation matrix on the group of special orthogonal matrices SO(3). The proposed techniques are extensively tested and their effectiveness compared resorting to simulated LADAR data sets under realistic noise conditions.

This paper presents a new ultra-short baseline (USBL) tightly-coupled integration technique to enhance error estimation in low-cost strap-down inertial navigation systems (INSs) with application to underwater vehicles. In the proposed strategy the acoustic array spatial information is directly exploited resorting to the extended Kalman filter implemented in a direct feedback structure. The determination and stochastic characterization of the round trip travel time are obtained resorting to pulse detection matched filters of acoustic signals modulated using spread-spectrum code division multiple access (CDMA). The performance of the overall navigation system is assessed in simulation and compared with a conventional loosely-coupled solution that consists of solving separately the triangulation and sensor fusion problems. From the simulation results it can be concluded that the proposed technique enhances the position, orientation, and sensors biases estimates accuracy

This paper addresses the problem of estimating the position and attitude of a rigid body when the available measurements consist only of distances (or ranges) between a set of body fixed beacons and a set of earth fixed landmarks. To this effect, a maximum likelihood (ML) estimator is derived by solving an optimization problem on the special Euclidean group SE(n);n = 2,3 using intrinsic gradient and Newton-like algorithms. The theoretical tools used borrow from optimization theory on Riemannian manifolds. Supported by recent results on performance bounds for estimators on Riemannian manifolds, the intrinsic variance lower bound (IVLB) is derived for the problem at hand. Simulation results are presented to illustrate the estimator performance and to validate the tightness of the IVLB in a wide range of signal to noise ratio scenarios