Michel Pelletier

Statically Fused Converted Position and Doppler Measurement Kalman Filters

The work presented in this paper makes two contributions for exploiting Doppler (range rate) measurements in tracking systems. First, a new linear filter, the converted Doppler measurement Kalman filter (CDMKF), is presented to extract nonlinear pseudostates from converted Doppler measurements (i.e., the product of the range measurements and Doppler measurements). The pseudostates are constructed from the converted Doppler and its derivatives. The linearly evolving equations of the pseudostates are derived for common target motion models. The second contribution of this paper is using the CDMKF along with the converted position measurement Kalman filter (CPMKF), in which only the position measurements are used, to establish a new filtering structure, statically fused converted measurement Kalman filters (SF-CMKF). The resulting states of CPMKF and CDMKF are combined by a static minimum mean squared error (MMSE) estimator, where the nonlinearity and correlation between the pseudostates and the Cartesian states are handled simultaneously, to yield the final state estimates. The dynamic nonlinear estimation problem is converted into dynamic linear estimation followed by static nonlinear fusion. The estimation accuracy can be enhanced by incorporating the Doppler measurements via the linear CDMKF, while the filtering stability can be improved by dealing with nonlinearity outside the filtering recursions. Monte Carlo simulations and comparison with the posterior Cramer-Rao bound demonstrate the effectiveness of the CDMKF and SF-CMKF.

Integrated Clutter Estimation and Target Tracking Using Poisson Point Process

In this paper, methods of tracking multiple targets in non-homogeneous clutter background is studied. In many scenarios, after detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region. On the other hand, in order to obtain accurate results, the target tracking filter requires information about clutters spatial density. Thus, non-homogeneous clutter point spatial density has to be estimated based on the measurement point set and tracking filters outputs. Also, due to the requirement of compatibility, it is desirable for this estimation method to be integrated into current tracking filters. In this paper, a recursive maximum likelihood method and an approximated Bayesian method are proposed to estimate the clutter point spatial density in non-homogeneous clutter background and both will in turn be integrated into Probability Hypothesis Density (PHD) filter. Here, non-homogeneous Poisson point processes, whose intensity function are assumed to be mixtures of Gaussian functions, are used to model clutter points. The mean and covariance of each Gaussian function is estimated and used in the update equation of the PHD filter. Simulation results show that the proposed methods are able to estimate the clutter point spatial density and improve the performance of PHD filter over non-homogeneous clutter background.

Multitarget Tracking with Doppler Ambiguity

A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms.

Synthesis of robust compliant motions based on impedance models

This paper presents a new approach to the problem of control synthesis and motion planning for compliant motions of manipulators. The scheme is based on decoupled linear impedance models of the robot and the environment, in which the errors due to control, task frame estimation, as well as environment uncertainty are explicitly taken into account. The technique consists of characterizing the position, velocity and force responses of the decoupled models, and expressing the task goals as constraints on these expressions. The impedance parameters of the controller are found by fitting uncertainty boxes inside the region of the parameter space that satisfies all task constraints. An experimental verification of this method is presented for the task of closing a circuit breaker.<<ETX>>

Telemanipulator design and optimization software

For many years, industrial robots have been used to execute specific repetitive tasks. In those cases, the optimal configuration and location of the manipulator only has to be found once. The optimal configuration or position where often found empirically according to the tasks to be performed. In telemanipulation, the nature of the tasks to be executed is much wider and can be very demanding in terms of dexterity and workspace. The position/orientation of the robots base could be required to move during the execution of a task. At present, the choice of the initial position of the teleoperator is usually found empirically which can be sufficient in the case of an easy or repetitive task. In the converse situation, the amount of time wasted to move the teleoperator support platform has to be taken into account during the execution of the task. Automatic optimization of the position/orientation of the platform or a better designed robot configuration could minimize these movements and save time. This paper will present two algorithms. The first algorithm is used to optimize the position and orientation of a given manipulator (or manipulators) with respect to the environment on which a task has to be executed. The second algorithm is used to optimize the position or the kinematic configuration of a robot. For this purpose, the tasks to be executed are digitized using a position/orientation measurement system and a compact representation based on special octrees. Given a digitized task, the optimal position or Denavit-Hartenberg configuration of the manipulator can be obtained numerically. Constraints on the robot design can also be taken into account. A graphical interface has been designed to facilitate the use of the two optimization algorithms.

The spline probability hypothesis density filter

The Probability Hypothesis Density Filter (PHD) is a multitarget tracker for recursively estimating the number of targets and their state vectors from a set of observations. The PHD filter is capable of working well in scenarios with false alarms and missed detections. Two distinct PHD filter implementations are available in the literature: the Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) and the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. The SMC-PHD filter uses particles to provide target state estimates, which can lead to a high computational load, whereas the GM-PHD filter does not use particles, but restricts to linear Gaussian mixture models. The SMC-PHD filter technique provides only weighted samples at discrete points in the state space instead of a continuous estimate of the probability density function of the system state and thus suffers from the well-known degeneracy problem. This paper proposes a B-Spline based Probability Hypothesis Density (S-PHD) filter, which has the capability to model any arbitrary probability density function. The resulting algorithm can handle linear, non-linear, Gaussian, and non-Gaussian models and the S-PHD filter can also provide continuous estimates of the probability density function of the system state. In addition, by moving the knots dynamically, the S-PHD filter ensures that the splines cover only the region where the probability of the system state is significant, hence the high efficiency of the S-PHD filter is maintained at all times. Also, unlike the SMC-PHD filter, the S-PHD filter is immune to the degeneracy problem due to its continuous nature. The S-PHD filter derivations and simulations are provided in this paper.

Integrated clutter estimation and target tracking using JIPDA/MHT tracker

In this paper, the problem of tracking multiple targets in unknown clutter background using the Joint Integrated Probabilistic Data Association (JIPDA) tracker and the Multiple Hypotheses Tracker (MHT) is studied. It is common in real tracking problems to have little or no prior information on clutter background. Furthermore, the clutter backgroundmay be dynamic and evolve with time. Thus, in order to get accurate tracking results, trackers need to estimate parameters of clutter background in each sampling instant and use the estimate to improve tracking. In this paper, incorporated with the JIPDA tracker or the MHT algorithm, a method based on Nonhomogeneous Poisson point processes is proposed to estimate the intensity function of non-homogeneous clutter background. In the proposed method, an approximated Bayesian estimate for the intensity of non-homogeneous clutter is updated iteratively through the Normal-Wishart Mixture Probability Hypothesis Density (PHD) filter technique. Then, the above clutter density estimate is used in the JIPDA algorithm and the MHT algorithm for multitarget tracking. It is demonstrated thorough simulations that the proposed clutter background estimation method improves the performance of the JIPDA tracker in unknown clutter background.

Synthesis of compliant motions in moving environments: experimental results

This paper presents a methodology to synthesize robot compliant motions in environments that can move or oscillate due to random disturbances. Motions of the environment are taken into account in the task frame position which is considered unknown and time-varying but bounded. The position, velocity and force responses of the coupled robot/environment system are determined and tasks are expressed as inequality constraints on these expressions. A set of controller parameters is found by fitting the largest possible uncertainty box inside the region of the impedance parameter space that satisfies all task constraints. Experimental results demonstrate the validity and robustness of the approach.

Computer vision-guided robotic system for electrical power lines maintenance

The paper presents several modules of a computer vision assisted robotic system for the maintenance of live electrical power lines. The basic scene of interest is composed of generic components such as a crossarm, a power line and a porcelain insulator. The system is under the supervision of an operator which validates each subtask. The system uses a 3D range finder mounted at the end effector of a 6 dof manipulator for the acquisition of range data on the scene. Since more than one view is required to obtain enough information on the scene, a view integration procedure is applied to the data in order to merge the information in a single reference frame. A volumetric description of the scene, in this case an octree, is built using the range data. The octree is transformed into an occupancy grid which is used for avoiding collisions between the manipulator and the components of the scene during the line manipulation step. The collision avoidance module uses the occupancy grid to create a discrete electrostatic potential field representing the various goals (e.g. objects of interest) and obstacles in the scene. The algorithm takes into account the articular limits of the robot and uses a redundant manipulator to ensure that the collision avoidance constraints do not compete with the task which is to reach a given goal with the end-effector. A pose determination algorithm called Iterative Closest Point is presented. The algorithm allows to compute the pose of the various components of the scene and allows the robot to manipulate these components safely. The system has been tested on an actual scene. The manipulation was successfully implemented using a synchronized geometry range finder mounted on a PUMA 760 robot manipulator under the control of Cartool.

A Gaussian mixture filter for target tracking with Doppler ambiguity

Target tracking with ambiguous Doppler measurements as well as position measurements is investigated. This paper presents a method using Gaussian Mixture representation of the Doppler measurement uncertainty. The conditional probability of target Doppler given an ambiguous Doppler measurement is approximated by a Gaussian sum of several possible unambiguous Doppler. Then the Gaussian Mixture filter based on the unscented Kalman filter (UKF) is presented to solve the problem of state estimation from measurements with Doppler ambiguity. Simulation results demonstrate the effectiveness of this approach.