Stereo Tracking and Servoing for Space Applications

This paper proposes a real-time, robust and efficient three-dimensional (3-D) model-based tracking algorithm for visual servoing. A virtual visual servoing approach is used for 3-D tracking. This method is similar to more classical nonlinear pose computation techniques. Robustness is obtained by integrating an M-estimator into the virtual visual control law via an iteratively reweighted least-squares implementation. The presented approach is also extended to the use of multiple cameras. Results show the method to be robust to occlusion, changes in illumination and mistracking.     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

A haptic representation system for a virtual plain wall

The purpose of this study is to develop a virtual wall display system for a walk simulator to grope its way in an 'invisible' situation, such as in a building filled with dense smoke, etc. To reproduce the realistic haptic sense of a building wall, the implementation of the wideness and the rigidity of wall are essential. Wideness of a virtual wall was realized by a hand-tracking control combined with a small wall panel which is mounted on a three-axis Cartesian manipulator. A 6-d.o.f. magnetic tracking system was utilized for the hand position tracking in the non-contact situation of the hand and the panel. In the contact situation, high rigidity of the wall was attained as stiffness in the normal direction is provided to the wall panel to represent the haptic sense of a rigid wall. Force-based tracking provides the low stiffness in the tangential direction to make the wall panel move easily along the direction of hand movement to represent a wide plain wall. A three-axis force sensor is attached on the wall panel to detect the contact force. The realization of smooth switching between both tracking controls provides the user with the haptic feel of the presence and continuity of a virtual wall. In addition, the frictional sensation has the effect of giving the system more reality. Experimental results have shown the effectiveness of the hybrid tracking method for the virtual wall system.     

SLAMMOT-SP: Simultaneous SLAMMOT and Scene Prediction

In recent years, SLAMMOT (simultaneous localization, mapping and moving object tracking) has attracted widespread attention in the mobile robot field. This paper proposes a new approach, SLAMMOT-SP, which combines SLAMMOT and scene prediction (SP). It extends the SLAMMOT problem to simultaneous map prediction and moving object trajectory prediction. The robot not only passively collects the data and executes SLAMMOT, but actively predicts the scene. The recursive Bayesian formulation of SLAMMOT-SP is derived for real-time operations. A generalized framework for tracking and predicting the moving objects is also proposed. Simulations and experiments show that the proposed SLAMMOT-SP is effective and can be performed in real-time.     

Investigations on the Hybrid Tracking Control of an Underactuated Autonomous Underwater Robot

The problem of trajectory tracking control of an underactuated autonomous underwater robot (AUR) in a three-dimensional (3-D) space is investigated in this paper. The control of an underactuated robot is different from fully actuated robots in many aspects. In particular, these robot systems do not satisfy Brockett's necessary condition for feedback stabilization and no continuous time-invariant state feedback control law exists that makes a specified equilibrium of the closed-loop system asymptotically stable. The uncertainty of hydrodynamic parameters, along with the coupled, nonlinear dynamics of the underwater robot, also makes the navigation and tracking control a difficult task. The proposed hybrid control law is developed by combining sliding mode control (SMC) and classical proportional–integral–derivative (PID) control methods to reduce the tracking errors arising out of disturbances, as well as variations in vehicle parameters like buoyancy. Here, a trajectory planner computes the body-fixed linear and angular velocities, as well as vehicle orientations corresponding to a given 3-D inertial trajectory, which yields a feasible 6-d.o.f. trajectory. This trajectory is used to compute the control signals for the three available controllable inputs by the hybrid controller. A supervisory controller is used to switch between the SMC and PID control as per a predefined switching law. The switching function parameters are optimized using Taguchi design techniques. The effectiveness and performance of the proposed controller is investigated by comparing numerically with classical SMC and traditional linear control systems in the presence of disturbances. Numerical simulations using the full set of nonlinear equations of motion show that the controller does quite well in dealing with the plant nonlinearity and parameter uncertainties for trajectory tracking. The proposed controller response shows less tracking error without the usually present control chattering. Some practical features of this control law are also discussed.     

Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling

Robots designed to interact socially with people require reliable estimates of human position and motion. Additional pose data such as body orientation may enable a robot to interact more effectively by providing a basis for inferring contextual social information such as people's intentions and relationships. To this end, we have developed a system for simultaneously tracking the position and body orientation of many people, using a network of laser range finders mounted at torso height. An individual particle filter is used to track the position and velocity of each human, and a parametric shape model representing the person's cross-sectional contour is fit to the observed data at each step. We demonstrate the system's tracking accuracy quantitatively in laboratory trials and we present results from a field experiment observing subjects walking through the lobby of a building. The results show that our method can closely track torso and arm movements, even with noisy and incomplete sensor data, and we present examples of social information observable from this orientation and positioning information that may be useful for social robots.     

An active contour model with shape regulation scheme

This paper presents an active method for locating target objects in images, which is aimed at improving the performance of detecting object boundaries by enhancing the behavioral characteristics of an active contour. The proposed active contour model simulates a mechanical system consisting of two main parts: the first is a rigid fixture, called the 'core', specifying the expected shape of target boundaries, while the second is an elastic rod attached to the rigid fixture. The elastic rod deforms or moves relative to the rigid core according to the classical laws of the mechanical system. When the initial contour is applied to an image data, it is attracted near the dominant image features, but tries to keep its home shape and simultaneously make the deformation smooth if a deformation is more natural for force equilibrium. This mechanism significantly improves the performance of detecting object boundaries in the presence of some disturbing image features. The active contour is scale invariant, thereby significantly relieving the difficulty in selecting proper values for the model parameters. The values for the model parameters can be selected to make the contour have the desired behaviors around the equilibrium position through the analysis of the vibration mode of the mechanical system. The performance of the proposed method is validated through a series of experiments, which include detection of heavily degraded objects, tracking of objects under non-rigid motion and comparisons with the original snake models.     

Vision based seam tracking system for underwater flux cored arc welding

Abstract

Manual underwater welding is usually time consuming, expensive and hard to perform because of the rigorous underwater environment. Automatic underwater welding can be performed faster at less cost and with higher quality. An automatic seam tracking system for underwater flux cored arc welding has been developed. It consists of a vision sensing module, a seam recognition module, a fuzzy controller (FC) and an X–Y travel platform. The vision sensing module can capture clear seam images during welding, successfully resolving the problem of welding arc interference during underwater flux cored arc welding. The seam recognition module filters, enhances and thresholds the seam images and then recognises the seam deviation angle with a three layer back propagation neural network. The FC outputs the control parameters according to the seam deviation angle and then controls the X–Y platform to drive the torch to the centre of the seam. In this study, three different welds were considered: a straight line, a kinked straight line and an S curved line. These welds were tracked real timely during underwater flux cored arc welding. The results show that this seam tracking system can meet the requirements of the automatic underwater flux cored arc welding.     

Seam tracking based on estimation of weld position using Kalman filtering

Abstract

A seam tracking method is presented based on the estimation of weld position during the gas tungsten arc welding process. Kalman filtering of the weld pool images from a visual sensor is applied to compute recursively the solution to the weld position equations which are established based on an estimation of the centroid position of the weld pool images. This centroid, the position of which corresponds with the weld position, is extracted as the measurement eigenvector. The evolution of the weld position data from the weld pool images can be described through an appropriate process model, so that the weld position can be detected by applying a Kalman filter. This allows adjustment of the welding torch position in real time, which may significantly reduce processing time and promote seam tracking accuracy. Simulations and actual welding experiments have demonstrated the effectiveness of the proposed algorithm in the presence of weld pool image noise and have demonstrated the robustness of weld position detection for seam tracking.