Longitudinal Motion Planning for Slung-Loads Using Simplified Models and Rapidly-Exploring Random Trees

Presented at the Sixth AHS International Specialists’ Meeting On Unmanned Rotorcraft Systems, Chandler, Arizona, January 20-22, 2015A randomized motion-planning approach to providing guidance for helicopters with under-slung loads is presented. Rapidly-exploring Random Trees are adapted to plan trajectories for simplified helicopter-load models. Four different planning models are tested against four different representative tasks. The poor performance of the baseline planner, and subsequent efforts to improve that performance shows the sensitivity of the RRT to proper sizing of the sampling area and amount of computation available. Further lines of potential research into optimizing planner performance and reducing computational cost are identified

Preliminary Evaluation of Rapidly-Exploring Random Trees for Sling-Load Flight Guidance

Presented at the 2nd Asia-Australia Rotorcraft Forum and 4th International Basic Research Conference on Rotorcraft Technology, Tianjin, China, September 8–11, 2013.Copyright © 2013 by the authors, Published with Permission.A novel approach to providing guidance for helicopters with under-slung loads is presented. Rapidly-exploring Ran- dom Trees are adapted to plan trajectories for simplified helicopter-load models. The algorithm is presented with a task to place a load in a specific location at a moment when the load is motionless, mimicing the actions of helicopter-based Christmas tree harvesting. The solutions provided by the RRT method vary in aggressiveness and precision, and are not ready for live implementation, but show promise in future development of this approach

Monocular Visual Mapping for Obstacle Avoidance on UAVs

Copyright © 2013 IEEEDOI: http://dx.doi.org/10.1109/ICUAS.2013.6564722An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results and results from recorded flight test data are presented to validate the algorithm

Monocular Visual Mapping for Obstacle Avoidance on UAVs

Copyright © 2014 SpringerDOI: http://dx.doi.org/10.1007/s10846-013-9967-7An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm

Fourteen Years of Autonomous Rotorcraft Research at the Georgia Institute of Technology

Presented at the 2nd Asia-Australia Rotorcraft Forum and 4th International Basic Research Conference on Rotorcraft Technology, Tianjin, China, September 8–11, 2013.Copyright ©2013 by the authors, Published with Permission.This paper presents a brief history and description of capabilities of the Georgia Tech Unmanned Aerial Vehicle Research Facility, while extracting and summarizing many significant and applicable results produced in the last fourteen years. Twenty-six selected publications are highlighted, which are representative of the research conducted at GT-UAVRF since 2000. The papers are divided into three groups: 1) development of a fault-tolerant adaptive flight control system, 2) development of vision-based navigation and control algorithms, and 3) special applications. For each group, the research and results are described, with references to the relevant paper(s)

The Current State of Business Intelligence in Academia: The Arrival of Big Data

In December 2012, the AIS Special Interest Group on Decision Support, Knowledge and Data Management Systems (SIGDSS) and the Teradata University Network (TUN) cosponsored the Business Intelligence Congress 3 and conducted surveys to assess academia’s response to the growing market need for students with Business Intelligence (BI) and Business Analytics (BA) skill sets. This panel report describes the key findings and best practices that were identified, with an emphasis on what has changed since the BI Congress efforts in 2009 and 2010. The article also serves as a “call to action” for universities regarding the need to respond to emerging market needs in BI/BA, including “Big Data.” The IS field continues to be well positioned to be the leader in creating the next generation BI/BA workforce. To do so, we believe that IS leaders need to continuously refine BI/BA curriculum to keep pace with the turbulent BI/BA marketplace

Flight Testing of Nap of-the-Earth Unmanned Helicopter Systems

This paper describes recent results from a partnership between the Sikorsky Aircraft Corporation and the Georgia Institute of Technology to develop, improve, and flight test a sensor, guidance, navigation, control, and real-time flight path optimization system to support high performance nap-of-the-Earth helicopter flight. The emphasis here is on optimization for a combination of low height above terrain/obstacles and high speeds. Multiple methods for generating the desired flight path were evaluated, including (1) a simple processing of each laser scan; and (2) a potential field based method. Simulation and flight test results have been obtained utilizing an onboard laser scanner to detect terrain and obstacles while flying at low altitude, and have successfully demonstrated obstacle avoidance in a realistic semi-urban environment at speeds up to 40 ft/s while maintaining a miss distance of 50 ft horizontally and vertically. These results indicate that the technical approach is sound, paving the way for testing of even lower altitudes, higher speeds, and more aggressive maneuvering in future work