Presents a listing of the Symposium organizing committee.

This research investigates different visual features for augmented reality (AR)–based assembly instructions. Since the beginning of AR research, one of its most popular application areas has been manual assembly assistance. A typical AR assembly application indicates the necessary manual assembly operations by generating visual representations of parts that are spatially registered with, and superimposed on, a video representation of the physical product to be assembled. Research in this area indicates the advantages of this type of assembly instruction presentation. This research investigates different types of visual features for different assembly operations. The hypothesis is that in order to gain an advantage from AR, the visual features used to explain a particular assembly operation must correspond to its relative difficulty level. The final goal is to associate different types of visual features to different levels of task complexity. A user study has been conducted in order to compare different visual features at different operation complexity levels. The results support the hypothesis.

The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. Note to Practitioners-This paper is inspired by the source seeking problem in which the signal emitted from the source is assumed to be very noisy, and the spatial distribution is assumed to be non-smooth. We focus our work specifically on electromagnetic sources. However, the strategies proposed in this paper are also applicable to other kinds of sources, for example, nuclear, radiological, chemical or biological. We develop a planner for a swarm of mobile agents that try to locate an unknown electromagnetic source. The mobile agents know their own positions and can measure the signal strength at their current location. They can share information among themselves, and plan for the next step. We propose a complete solution to ensure the effectiveness of PSO in complex environments where collisions may occur. We incorporate static and dynamic obstacle avoidance strategies in PSO to make it fully applicable to real-world scenario. We validate the proposed technique on an experimental testbed. As a part of our future work, we will extend the technique to locate multiple sources of different kinds.

Selection of moving targets is a common, yet complex task in human-computer interaction (HCI) and virtual reality (VR). Predicting user intention may be beneficial to address the challenges inherent in interaction techniques for moving-target selection. This article extends previous models by integrating relative head-target and hand-target features to predict intended moving targets. The features are calculated in a time window ending at roughly two-thirds of the total target selection time and evaluated using decision trees. With two targets, this model is able to predict user choice with up to ~ 72% accuracy on general moving-target selection tasks and up to ~ 78% by also including task-related target properties.

This study incorporated a dual-task paradigm, in which participants were asked to perform basic locomotion tasks with one of three interfaces while remembering a sequence of either spatial or verbal items. Interfaces varied in similarity to natural body movements. Stopping performance was compromised when concurrently remembering a spatial, but not verbal, sequence. Also users exhibited lower performance on spatial memory tasks while using more unnatural locomotion interfaces. These results confirm that semi-natural locomotion interfaces require spatial working memory resources and thus locomotion interfaces compete with ongoing spatial tasks, as opposed to those requiring verbal resources or general attention resources.

A novel teleoperation interface is introduced featuring an integrated virtual reality based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The system addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view, allowing the operator to navigate in a continuous fashion. It also incorporates temporary autonomous operation of the vehicle enabling local obstacle navigation and automatic re-establishment of its teleoperated state. Finally, the system provides real time update of the virtual environment based on anomalies encountered by the vehicle. Future work is focused on incorporating dynamic moving objects in the environment

A system of algorithms is presented for material removal simulation, dimensional error assessment and automated correction of five-axis numerically controlled (NC) milling tool paths. The methods are based on a spatial partitioning technique which incorporates incremental proximity calculations between milled and design surfaces. Hence, in addition to real-time animated five-axis milling simulation, milling errors are measured and displayed simultaneously. Using intermediate error assessment results, a reduction of intersection volume algorithm is developed to eliminate gouges on the workpiece via tool path correction. Finally, the view dependency typical of previous spatial partitioning-based NC simulation methods is overcome by a contour display technique which generates parallel planar contours to represent the workpiece, thus enabling dynamic viewing transformations without reconstruction of the entire data structure.