Farhad Ghassemi

An accelerometer-based joint angle sensor for heavy-duty manipulators

An indirect, self-calibrating, easy to install, and robust joint angle sensing method for heavy-duty manipulators is presented in this paper. This method is suitable for the harsh working environment of these machines where conventional contact-type angle sensors cannot be deployed, or problems are associated with their use. The approach is based on processing the outputs of a pair of biaxial accelerometers placed very close to the joint axis on the adjacent links. In the proposed technique, joint angles are obtained without integrating the accelerometer outputs to avoid measurement error accumulation over a long period of time. Two calibration procedures are also described for accelerometers to ensure the accuracy of their measurements. The experimental results attest to the efficiency and accuracy of the new angle sensing mechanism.

Design and Calibration of an Integration-Free Accelerometer-Based Joint-Angle Sensor

A methodology has been developed for indirect, noncontact, and dynamic sensing of angles for robotic applications. Two accelerometers are placed on the adjacent links close to the joint axis, and their outputs are processed to estimate the joint angle. In the proposed technique, the joint angles are obtained without integrating the accelerometer outputs. To ensure accuracy of accelerometer readings, two calibration procedures for the accelerometers are presented, which can easily be implemented in place. Both these methods solve a nonlinear least squares problem to adjust the offset parameters of the accelerometers. The accelerometer-based angle sensor is particularly suitable for the harsh working environment of heavy-duty manipulators, where conventional contact-type angle sensors cannot be deployed or problems are associated with their use. The performance of the new sensor is studied and compared with the performance of digital resolvers in two applications, involving the position control and dynamic payload measurement of a miniexcavator. The experimental results attest to the efficiency and accuracy of the new angle-sensing mechanism.

Haptic rendering of planar rigid-body motion using a redundant parallel mechanism

We present a system for rendering planar rigid-body motion by means of a redundant parallel mechanism. The device design, the control architecture and the passive virtual environment simulation are presented. The system is used to compare various virtual walls and friction models proposed for haptic applications. In addition, the reset-integrator dry friction model proposed by Haessig and Friedland (1991) is implemented in a haptic interface for the first time.

The entropy power of a sum is fractionally superadditive

It is shown that the entropy power of a sum of independent random vectors, seen as a set function, is fractionally superadditive. This resolves a conjecture of the first author and A. R. Barron, and implies in particular all previously known entropy power inequalities for independent random variables. It is also shown that, for general dimension, the entropy power of a sum of independent random vectors is not supermodular.

Decentralized Node Selection for Localization in Wireless Unattended Ground Sensor Networks

In this paper, we present a decentralized algorithm for node selection in unattended ground sensor networks when the goal is to minimize the localization error. The motivation behind this approach is that the sensing nodes can be better judges for the quality of their measurements than the central query node. The sensing nodes transmit their measurements to the query node when the information gain realized from their measurement surpasses a price set for transmission. The information gain is defined as the mutual information between the prior density of the target position and the measurement. The price can be set globally by the query node or locally by the sensing nodes.

A method for constructing the observer trajectory in bearings-only tracking of targets with a Markovian model

We propose a method for constructing the observer trajectory in bearings-only tracking. This method is applicable to any target with a Markovian model but in particular, we study it for nearly-constant velocity targets. The proposed method is built upon a family of optimal trajectories obtained for the localization of stationary targets. These trajectories can be parameterized by a parameter known as range-to-baseline ratio. For moving targets, at each moment, the range-to-baseline ratio is computed and the appropriate trajectory is pursued based on this ratio and current estimates of the target bearing and course. We study the behavior of this method by simulation.

A Stochastic Search Approach for UAV Trajectory Planning In Localization Problems

We discuss the off-line and on-line aspects of trajectory planning in bearings-only localization. Assuming that there are m(ges 1) moveable sensors (e.g. UAVs), which fly in closed trajectories, the aim is to determine the optimal shape of the trajectory. We investigate the properties of closed optimal trajectories in the off-line problem and show that these solutions are invariant under a scaling transformation of the problem parameters. This result is used to numerically derive a set of solutions for the normalized parameters. These solutions are then used in a stochastic search algorithm which randomly explores the trajectories but spends the largest amount of time in the optimal trajectory

Symmetric probabilistic values for identifying informative sensors

In this paper, we show how the notion of symmetric probabilistic values from cooperative game theory can be used in a sensor network to identify the sensors that are relatively more informative than others. We note that parameter estimation in a sensor network can be modeled as a cooperative game, where a metric of estimation accuracy assigns a value to each subset of sensors. Symmetric probabilistic values are then known to be indicators of the relative power of players in cooperative games. Motivated by this, we define a power index for sensors based symmetric probabilistic values. While generally any metric of estimation accuracy can be used for computing power indices, it is noted that by choosing the determinant of the Fisher information matrix, the computational complexity associated with power indices gracefully increases with the number of sensors. The formulas are explicitly provided for computing the Banzhaf value and the Shapley value, two well-known symmetric probabilistic values. A target whose parameter is being estimated by the sensor network can use power indices to identify and act against the informative sensors. As an important application in this regard, the power indices of sensors are computed in bearings-only and range-only target localization.