A Robot Mounted Electromagnetic Induction System for Identification of a UXO Free Corridor
Type of Degreethesis
MetadataShow full item record
This thesis gives a complete overview of a time domain electromagnetic induction system developed for identification of a UXO free corridor. An EMI sensor typically consists of the transmitter and receiver coils to induct and capture the induced signal. Receiver coil amplifier circuitry is used to enhance the captured signal and data acquisition hardware and software are employed to carefully acquire and analyze the received signal to achieve an accurate understanding of the nature of the buried object. The thesis gives a detailed description of the sensor with above mentioned components and furthermore addresses GPS location tagging of the buried targets. A pulser was used to drive a short duration current pulse through the transmitter coil which is a 35 turn AWG 10 standard wire rectangular loop. Three different receiver coil configurations were used to get a better understanding of the detection capability based on the number of receiver coil arrays. The receiver amplifier used was a precision ultra low noise operational amplifier with low DC offset. All the data was acquired in real time with National Instruments compact RIO controller and Labview FPGA programming. In order to validate the detection capabilities of the sensor, dynamic field measurements were conducted along 5 equidistant lanes with about 35 buried ferrous and non ferrous targets. The tests were conducted at the Air Force Research Laboratory Robotics Division in Panama city, Florida during December 2008. The set up consisted of a 4 wheeled segway robot connected to a fiberglass trailer carrying the transmitter and receiver coils. The segway was maneuvered remotely using a wireless joystick and the acquired receiver coil data along with the GPS NMEA message was communicated wirelessly to a networked PC using a 802.11 wireless router. Four, two and one coil pair receiver configurations were used to collect the target response.Each run consisted of data collection starting on lane 1 and following in a serpentine fashion on the remaining lanes. All the targets except a deeply buried 105mm target (at 120cm) were detected by at least one coil configuration, and the sensor proved to be a reliable detection and GPS tagging device. The signals from two “identically” wound receiver coils were subtracted using a differential amplifier. This approach allows one to identify the approximate down track location of the target since a sharp null occurs when the target is located approximately between the coils. The responses from ferrous and non ferrous targets during the early time have opposite polarities during the on time of the current pulse which provides a method to easily discriminate between the two types of metal. It was observed that objects at a depth of 120cm (3.937ft) had a low amplitude response. The observation suggests that improvement in the receiver coil amplifier and better post signal processing techniques might further improve the detection abilities with respect to deeply buried targets. Long metal bars(fiducials) were used to mark the beginning and end of every lane but unfortunately they were placed too close to the first and last targets in each lane thus somewhat compromising the data collected from the end-lane targets. The single receiver coil configuration response was the weakest among the three configurations probably because it had to be oriented in a cross track direction to fit atop the transmitter coil. The GPS antenna was placed in the segway robot and by knowing the distance between the antenna and the center of the coils, a latitude correction could be incorporated to achieve accurate location of the coil. Subsequent research, after the December 2008 tests at AFRL, was conducted to optimize the receiver coil amplifier. This work resulted in the ability to capture the exponential decaying response of the target over 5 orders of magnitude. High dynamic range data like this is required in order to discriminate between UXO like objects and metallic clutter. In addition to the efforts to develop and enhance the above mentioned sensor, the real time data acquisition system along with the programming involved is also described in detail in this thesis.