Robots are already being used in a variety of applications, including the military battlefield. As robotic technology continues to advance, those applications will increase, as will the demands on the associated network communication links. Two experiments investigated the effects of communication latency on the control of a robot across four Levels Of Automation (LOAs), (1) full teleoperation, (2) guarded teleoperation, (3) autonomous obstacle avoidance, and (4) full autonomy. Latency parameters studied included latency duration, latency variability, and the "direction" in which the latency occurs, that is from user-to-robot or from robot-to-user. The results indicate that the higher the LOA, the better the performance in terms of both time and number of errors made, and also the more resistant to the degrading effects of latency. Subjective reports confirmed these findings. Implications of constant vs. variable-latency, user-to-robot vs. robot-to-user latency, and latency duration are also discussed.

This paper describes a method for tracking human body motion from multiple views in real-time. The method extracts silhouettes in each view using background subtraction, and then intersects the visual hulls generated by the silhouettes to create a set Of voxels. The voxels then exert attractive forces on a kinematic model of the human body to align the model with the voxels. The linked nature of the model allows tracking of partially occluded limbs. The size parameters of the kinematic model are determined automatically during an initialization phase. The kinematic model also incorporates velocity, joint angle, and self collision limits. The entire system with four cameras runs on a single PC in real-time at 20 frames per second. Experiments are presented comparing the performance of the system on real and synthetic imagery to ground truth data.

The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.

A three-dimensional world model is crucial for many robotic tasks. Modeling techniques tend to be either fully manual or autonomous. Manual methods are extremely time consuming but also highly accurate and flexible. Autonomous techniques are fast but inflexible and, with real-world data, often inaccurate. The method presented in this paper combines the two, yielding a highly efficient, flexible, and accurate mapping tool. The segmentation and modeling algorithms that compose the method are specifically designed for industrial environments, and are described in detail. A mapping system based on these algorithms has been designed. It enables a human supervisor to quickly construct a fully defined world model from unfiltered and unsegmented real-world range imagery. Examples of how industrial scenes are modeled with the mapping system are provided.