632 research outputs found
Intelligent Robotic Behaviors for Landmine Detection and Marking
This article discusses experimental results achieved with a robotic countermine system that utilizes autonomous behaviors and a mixed-initiative control scheme to address the challenges of detecting and marking buried landmines. By correlating aerial imagery and ground-based robot mapping, the interface provides context for the operator to task the robot. Once tasked, the robot can perform the search and detection task without the use of accurate global positioning system information or continuous communication with the operator. Results show that the system was able to find and mark landmines with a very low level of human involvement. In addition, the data indicates that the robotic system may be able to decrease the time to find mines and increase the detection, accuracy and reliability
Planetary rovers and data fusion
This research will investigate the problem of position estimation for planetary rovers.
Diverse algorithmic filters are available for collecting input data and transforming
that data to useful information for the purpose of position estimation process. The
terrain has sandy soil which might cause slipping of the robot, and small stones and
pebbles which can affect trajectory.
The Kalman Filter, a state estimation algorithm was used for fusing the sensor data
to improve the position measurement of the rover. For the rover application the
locomotion and errors accumulated by the rover is compensated by the Kalman
Filter. The movement of a rover in a rough terrain is challenging especially with
limited sensors to tackle the problem. Thus, an initiative was taken to test drive
the rover during the field trial and expose the mobile platform to hard ground and
soft ground(sand). It was found that the LSV system produced speckle image and
values which proved invaluable for further research and for the implementation of
data fusion.
During the field trial,It was also discovered that in a at hard surface the problem
of the steering rover is minimal. However, when the rover was under the influence
of soft sand the rover tended to drift away and struggled to navigate.
This research introduced the laser speckle velocimetry as an alternative for odometric
measurement. LSV data was gathered during the field trial to further simulate under
MATLAB, which is a computational/mathematical programming software used for
the simulation of the rover trajectory. The wheel encoders came with associated
errors during the position measurement process. This was observed during the
earlier field trials too. It was also discovered that the Laser Speckle Velocimetry
measurement was able to measure accurately the position measurement but at the
same time sensitivity of the optics produced noise which needed to be addressed as
error problem.
Though the rough terrain is found in Mars, this paper is applicable to a terrestrial
robot on Earth. There are regions in Earth which have rough terrains and regions
which are hard to measure with encoders. This is especially true concerning icy
places like Antarctica, Greenland and others.
The proposed implementation for the development of the locomotion system is to
model a system for the position estimation through the use of simulation and collecting data using the LSV. Two simulations are performed, one is the differential
drive of a two wheel robot and the second involves the fusion of the differential drive
robot data and the LSV data collected from the rover testbed. The results have
been positive. The expected contributions from the research work includes a design
of a LSV system to aid the locomotion measurement system.
Simulation results show the effect of different sensors and velocity of the robot. The
kalman filter improves the position estimation process
Unmanned Aerial Systems: Research, Development, Education & Training at Embry-Riddle Aeronautical University
With technological breakthroughs in miniaturized aircraft-related components, including but not limited to communications, computer systems and sensors, state-of-the-art unmanned aerial systems (UAS) have become a reality. This fast-growing industry is anticipating and responding to a myriad of societal applications that will provide new and more cost-effective solutions that previous technologies could not, or will replace activities that involved humans in flight with associated risks.
Embry-Riddle Aeronautical University has a long history of aviation-related research and education, and is heavily engaged in UAS activities. This document provides a summary of these activities, and is divided into two parts. The first part provides a brief summary of each of the various activities, while the second part lists the faculty associated with those activities. Within the first part of this document we have separated UAS activities into two broad areas: Engineering and Applications. Each of these broad areas is then further broken down into six sub-areas, which are listed in the Table of Contents. The second part lists the faculty, sorted by campus (Daytona Beach-D, Prescott-P and Worldwide-W) associated with the UAS activities. The UAS activities and the corresponding faculty are cross-referenced.
We have chosen to provide very short summaries of the UAS activities rather than lengthy descriptions. If more information is desired, please contact me directly, or visit our research website (https://erau.edu/research), or contact the appropriate faculty member using their e-mail address provided at the end of this document
Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks
A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned
Unmanned Robotic Systems and Applications
This book presents recent studies of unmanned robotic systems and their applications. With its five chapters, the book brings together important contributions from renowned international researchers. Unmanned autonomous robots are ideal candidates for applications such as rescue missions, especially in areas that are difficult to access. Swarm robotics (multiple robots working together) is another exciting application of the unmanned robotics systems, for example, coordinated search by an interconnected group of moving robots for the purpose of finding a source of hazardous emissions. These robots can behave like individuals working in a group without a centralized control
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