Generalized recognition of single-ended contact formations for use in automated assembly operations

Robots are preferred over any other form of automated machines for assembly tasks due to their capability of being programmed to perform a variety of tasks. However, in the present day industries, the turn around time for new designs have dramatically reduced. Therefore, the need for robots which can adapt its teaching and programming to new situations is strongly felt. This is especially true in the tasks such as assembly operations, which involve the robot making frequent contacts with its environment. This research addresses the problems that arise due to small changes in the work settings after the system has been programmed or trained. In an industry setting it is very likely that changes such as orientation and translation of the grasped object with respect to the robot axes can occur due to many unforeseen causes. The research here is focused on generalizing a Hybrid Control System, in which an assembly skill is described as a sequence of qualitative states and the desired transition between the states. In this case, the qualitative state takes the form of a single-ended contact formation, which describes how a grasped object touches its environment. Skill acquisition involves learning the sequence of qualitative states, the transition between those states, and the mapping from the sensor signals to the qualitative states. The authors discuss impact of changes in the orientation and the position of the grasped object with respect to the robot axes on the recognition of these qualitative states. They also propose a method of decreasing the performance degradation caused by this orientation change in recognition of these qualitative states, by adapting to the new situation with as minimum retraining as possible. Experimental results are presented which illustrate and validate the approach

Modeling Urban Warfare: Joint Semi-Automated Forces in Urban Resolve

The United States military is performing operations in urban environments with increasing frequency. Current Department of Defense doctrine is poorly suited to train and equip today\u27s warriors with the tools and experience necessary to fight and win in modern sprawling cities. In order to close the gap, the U.S. Joint Forces Command\u27s Joint Experimentation Directorate led an effort to run a massively distributed simulation of a synthetic urban environment utilizing human-in-the-loop operators called URBAN RESOLVE. The synthetic environment simulated the city of Jakarta with over 1,000,000 buildings and structures and over 120,000 civilian entities. A Red force retreated into the city while a Blue force attempted to determine the enemy\u27s Order of Battle. The exercise generated over 3.7 terabytes of data in seven distinct trials. This research evaluated the time required to identify targets after detection and the accumulation of identifications over time, and searched for trends between the seven design trials and between target groups. Two trends emerged from this research. First, there was a notable difference in the time required to identify a target once it has been detected based on its target group. Second, two design trials that are expected to demonstrate show counter-intuitive results

Oceanus.

v. 42, no. 1 (2000

COUNTER-UNMANNED AERIAL VEHICLES STUDY: SHIPBOARD LASER WEAPON SYSTEM ENGAGEMENT STRATEGIES FOR COUNTERING DRONE SWARM THREATS IN THE MARITIME ENVIRONMENT

This thesis studied engagement strategies for countering maritime drone swarm threats using a shipboard laser weapon system (LWS). The thesis examined maritime drone swarm threats to define parameters that characterize the different types of drone swarms expected in the near future. The thesis explored swarm attack formations, defined two potential heterogeneous swarm scenarios, and proposed five engagement strategies involving the order in which a shipboard laser weapon system would fire upon drones in a swarm threat. Modeling and simulation data was collected from the NPS Modeling Virtual Environments and Simulation (MOVES) Swarm Commander Tactics program to study the efficacy of swarm formation and engagement strategies. The results reinforce that the size of the swarm and formations used significantly affect the success rate of the attacking swarm. The complexity of the situation further increases when facing heterogeneous swarms. The results show that the success rate shifts severely in favor of the attacking swarm when using a simple heterogeneous decoy attack. When altering the LWS engagement strategy to counter this, there is a substantial reversal of success rate, which nearly changes the outcome in favor of the defending ship. This information amplifies the need to explore swarm attack and defense tactics that will organically develop with heterogeneous swarms and LWS use.ONR, Arlington, VA 22217Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Sensor-Based Activity Recognition and Performance Assessment in Climbing: A Review

In the past decades, a number of technological developments made it possible to continuously collect various types of sport activity data in an unobtrusive way. Machine learning and analytical methods have been applied to flows of sensor data to predict the conducted sport activity as well as to calculate key performance indicators. In that scenario, researchers started to be interested in leveraging pervasive information technologies for sport climbing, thus allowing, in day-to-day climbing practice, the realization of systems for automatic assessment of a climber’s performance, detection of injury risk factors, and virtual coaching. This article surveys recent research works on the recognition of climbing activities and the evaluation of climbing performance indicators, where data have been acquired with accelerometers, cameras, force sensors, and other types of sensors. We describe the main types of sensors and equipment adopted for data acquisition, the techniques used to extract relevant features from sensor data, and the methods that have been proposed to identify the activities performed by a climber and to calculate key performance indicators. We also present a classification taxonomy of climbing activities and of climbing performance indicators, with the aim to unify the existing work and facilitate the comparison of methods. Moreover, open problems that call for new approaches and solutions are here discussed. We conclude that there is considerable scope for further work, particularly in the application of recognition techniques to problems involving various climbing activities. We hope that this survey will assist in the translation of research effort into intelligent environments that climbers will benefit from

Application of Remote Sensing to the Chesapeake Bay Region. Volume 2: Proceedings

A conference was held on the application of remote sensing to the Chesapeake Bay region. Copies of the papers, resource contributions, panel discussions, and reports of the working groups are presented