A. Alcocer

Study and implementation of an EKF GIB-based underwater positioning system

The paper addresses the problem of estimating the position of an underwater target in real time. In the scenario adopted, the target carries a pinger that emits acoustic signals periodically, as determined by a very high precision clock that is synchronized with GPS, prior to system deployment. The target is tracked from the surface by using a system of four buoys equipped with hydrophones and electronic circuitry that measures the times of arrival of the acoustic signals emitted by the pinger or, equivalently, the four target-to-buoy range measurements (a commercial version of this setup is the GIB system). Due to the finite speed of propagation of sound in water, these measurements are obtained with different latencies. The paper tackles the problem of underwater target tracking in the framework of extended Kalman filtering by relying on a purely kinematic model of the target. The paper further shows also how the differently delayed measurements can be merged using a back and forward fusion approach. A measurement validation procedure is introduced to deal with dropouts and outliers. Simulation as well as experimental results illustrate the performance of the filter proposed.

Force Estimation and Control in Robot Manipulators

Abstract In this paper we present some results on model based force estimation and how these estimates can be integrated in a common robot force control scheme. A generalization of the force estimation method proposed in [Hacksel and Salcudean, 1994] is done and a force observer able to follow ramp environmental forces is introduced. An extension of this method for robotic manipulators is also experimentally verified using an industrial robot with an open control system architecture during a force control task.

Optimal Sensor Placement for Underwater Target Positioning with Noisy Range Measurements

Abstract Autonomous underwater vehicles (AUVs) are becoming ubiquitous due in part to the flexibility and versatility that a number of them display in the execution of individual and cooperative tasks. These characteristics, coupled with the fact that their use avoids placing human lives at risk, makes them quite attractive in a number of missions. Central to the operation of some classes of AUVs is the availability of a good underwater positioning system to localize one or more vehicles simultaneously based on information received on-board a support ship or an autonomous surface system. Speaking in loose terms, we are interested in determining the optimal geometric configuration of a surface sensor network that will, in a well defined sense, maximize the range-related information available for underwater target positioning. To this effect, we assume that the range measurements are corrupted by white Gaussian noise, the variance of which is distance-dependent. The Fisher Information Matrix and the maximization of its determinant are used to determine the sensor configuration that yields the most accurate positioning of the target. An explicit analytical result is first obtained for a three sensor network and then extended to a generic “n„ sensor network. It is shown that the optimal configuration lends itself to an interesting geometrical interpretation and that the “spreading„ of the configuration depends directly on the intensity of the range measurement noise and the target depth. Simulation examples illustrate the key results derived.

Estimation of Attitude and Position from Range-Only Measurements using Geometric Descent Optimization on the Special Euclidean Group

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

Joint positioning and navigation aiding system for underwater robots

This paper proposes a new joint positioning and navigation aiding system for underwater robots. In the scenario adopted, a submersed target carries a pinger that emits acoustic signals periodically, as determined by a low precision clock. The target is tracked from the surface by a set of buoys equipped with acoustic hydrophones/projectors, GPS receivers, and electronic circuitry that measures the times of arrival of the acoustic signals emitted by the pinger. Once an estimate for the underwater target position is computed, the buoys transmit this information back to the target indirectly, without resorting to an acoustic modem. This is done by encoding the information in the relative times of emission of acoustic pulses, as if they were emitted by a set of buoys remaining in fixed positions agreed upon in advance with the target. The times of arrival of these pulses are used by the target as aiding data to be fused with other motion data available onboard, in a local navigation system. The scheme proposed allows for the compensation of the: (i) displacement experienced by the surface buoys, which are not required to be moored, (ii) clock bias due to the low accuracy clock onboard the target, and (iii) motion of the target during the time interval it takes to complete a tracking-positioning cycle, as predicted by a dynamic target model implemented in the local positioning system. Simulation results are presented to assess the performance of both the positioning tracker and an onboard simplified integrated navigation system.

A dynamic estimator on SE(3) using range-only measurements

This paper addresses the problem of estimating the attitude and position of a rigid body when the available measurements consist only of the relative distances between a set of body fixed beacons and a set of Earth fixed landmarks. The proposed solution is given in terms of a dynamical system evolving on the Special Euclidean group SE(3), the trajectories of which are shown to locally converge to the actual attitude and position of the rigid body. Local asymptotic stability of the dynamical system is proven by using a suitable Lyapunov function, under the assumption that there is a set of noncoplanar landmarks and beacons. Simulation results are shown to illustrate the behaviour of the proposed estimator.

Maximum Likelihood Attitude and Position Estimation from Pseudo-Range Measurements using Geometric Descent Optimization

This paper addresses the problem of estimating the attitude and the position of a rigid body when the available measurements consist only of pseudo-ranges between a set of body fixed beacons and a set of earth fixed landmarks. To this effect, a maximum likelihood (ML) estimator is formulated as an optimization problem on the parameter space Theta = SE(3) times Ropfp corresponding to the attitude and position of the rigid body as well as a set of biases present in the pseudo-range equations. Borrowing tools from optimization on Riemannian manifolds, intrinsic gradient and Newton-like algorithms are derived to solve the problem. The rigorous mathematical setup adopted makes the algorithms conceptually simple and elegant; furthermore, the algorithms do not require the artificial normalization procedures that are recurrent in other estimation schemes formulated in Euclidean space. 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

Diffusion-Based Trajectory Observers with Variance Constraints

Abstract Diffusion-based trajectory observers have been recently proposed as a simple and efficient framework to solve diverse smoothing problems in underwater navigation. For instance, to obtain estimates of the trajectories of an underwater vehicle given position fixes from an acoustic positioning system and velocity measurements from a DVL. The observers are conceptually simple and can easily deal with the problems brought about by the occurrence of asynchronous measurements and dropouts. In its original formulation, the trajectory observers depend on a user-defined constant gain that controls the level of smoothing and is determined by resorting to trial and error. This paper presents a methodology to choose the observer gain by taking into account a priori information on the variance of the position measurement errors. Experimental results with data from an acoustic positioning system are presented to illustrate the performance of the derived observers.