A Hardware-In-the-Loop Simulation and Test for Unmanned Ground Vehicle on Indoor Environment

The Army’s chemical, biological, radiological, nuclear, and explosives (CBRNE) units respond to the any threat involving CBRNE elements. Their missions often involve the search and identification of radiation sources in a compromised facility. A major concern with this mission is the survivability of the Initial Entry Team, who is tasked with surveying the volatile indoor environment for data. The creation of a system to assist in, and expediate, the process of initial entry will greatly increase the health and welfare of the team. In order to localize and detect radiation in a pot-entially contaminated indoor environment, our team will develop the RADBOT, an unmanned, tethered robot that can de-tect and map radiation. This paper will summarize the research, design, testing, and results for the development of the RADBOT system.

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Heterogeneous Bi-Directional Cooperative Unmanned Vehicles for Obstacle Avoidance

Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control ◽

10.1115/dscc2016-9645 ◽

2016 ◽

Cited By ~ 1

Author(s):

Jonathan Lwowski ◽

Liang Sun ◽

Daniel Pack

Keyword(s):

Obstacle Avoidance ◽

Indoor Environment ◽

Sliding Mode ◽

Unmanned Vehicles ◽

Unmanned Ground Vehicle ◽

A Algorithm ◽

Ground Vehicle ◽

Cluttered Environment ◽

Aerial View ◽

Stable Sliding

In this paper, we present a novel bi-directional cooperative obstacle avoidance system of heterogeneous unmanned vehicles, consisting of an unmanned ground vehicle (UGV) and a microaerial vehicle (MAV), equipped with cameras, operating in an indoor environment without Global Positioning System (GPS) signals. The system demonstrates the synergistic relationship between the two platforms by sharing different perspectives and information collected independently using their on-board sensors in performing a navigation task in an indoor environment, including avoiding obstacles and entering narrow pathways. The MAV uses an aerial view of the environment to develop an obstacle free path for the UGV using the A* algorithm. The UGV deploys the planned path in conjunction with information gathered from its own front facing camera to navigate through a cluttered environment using a Lyapunov stable sliding mode controller. The UGV is responsible for detecting low and narrow pathways and to guide the MAV to move through them. The bidirectional cooperation has been tested in hardware as well as in simulation, showing the system’s effectiveness.

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Visual servoing and inception for a micro unmanned ground vehicle in the indoor environment

10.14711/thesis-991012662668603412 ◽

2018 ◽

Author(s):

Sui Pang

Keyword(s):

Indoor Environment ◽

Visual Servoing ◽

Unmanned Ground Vehicle ◽

Ground Vehicle

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Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning

Sensors ◽

10.3390/s18103419 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3419 ◽

Cited By ~ 3

Author(s):

Yitang Peng ◽

Xiaoji Niu ◽

Jian Tang ◽

Dazhi Mao ◽

Chuang Qian

Keyword(s):

Indoor Environment ◽

Signal Strength ◽

Indoor Positioning ◽

Received Signal Strength ◽

Kriging Interpolation ◽

Unmanned Ground Vehicle ◽

Received Signal Strength Indicator ◽

Ground Vehicle ◽

Fingerprint Database ◽

Signals Of Opportunity

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

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