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Title: Robotic and Mobile Sensor Systems for Structural Health Monitoring Authors:

Dryver Huston1 Brain Esser1 Jonathan Miller1 Xiaoguang Wang2 1

Mechanical Engineering Department Civil Engineering Department University of Vermont Burlington, VT 05405-0156 USA 2

ABSTRACT This paper describes the use of robotic and other mobile sensor systems for adaptive structural health monitoring. Sensing parameters on large structures poses many challenges due to size, geometry, and the ability to measure important events and parameters in a timely manner. Damage is often localized, can occur in unexpected places and at unexpected times. Mobile robotic and deployable sensor systems offer the opportunity to adapt to structural changes and events as they emerge and to measure where it is impractical or dangerous to humans. Some of the design issues for mobile sensor systems are discussed. Results from a study of four different beam crawling robots will be presented. INTRODUCTION Effectively monitoring the health and performance of structures is often difficult because of the location and timing of events of interest, i.e. damage, are usually not known a priori. Without knowledge of how a structure will fail, statically placing an array of sensors to monitor the structure can be expensive and often ineffective. If the sensors can be rapidly deployed to adapt to emergent critical structural situations, then better performing structural health monitoring systems are realizable. Humans have been used as adaptive sensing systems for millennia. Trained humans have the ability to recognize the significance of complex situations and to focus their attention, i.e. squint, at critical situations. However, many situations arise where visual inspection by humans is not practical. These include high-risk and hard to reach locations. Also, humans have biases and imperfect memories. _______________ Dryver Huston, Brian Esser and Jon Miller, Mechanical Engineering; Xiaoguang Wang, Civil Engineering; University of Vermont, Burlington, VT 05405

Mobile sensing systems fall into two main categories. The first category contains those sensors that can be readily attached and detached from a structure at emergent points of interest. The second category includes standoff noncontacting imaging type sensors. These have the possibility of easily moving around a structure by moving or pointing and squinting the sensing system, such as in mobile ground penetrating radar for the inspection of pavements. A variety of mobile point sensors can be attached or inserted into the structure and then easily moved. A requirement is that the sensor must be easily attached and removed from the structure. Examples are clamp-on strain gages, magnetically mounted accelerometers, or glue-on crack-width monitoring gages, and the Microstrain DVRT [1]. Figure 1 shows a clamp-on Z-gage that measures strain with an isolated shear plate. There are many other sensor types, if they are sufficiently miniaturized, could be used, such as eddy current and ultrasound. A major issue in the implementation of mobile adaptive sensing systems is mobility. Most structures are individually unique and pose many geometric mobility constraints. Two primary options exist. The first is to use humans. The second is to use automated procedures, such as robots. Humans have many advantages, such as they have the ability to recognize and react to complex situations in a manner that is very difficult to program into a machine. Robotic systems can be used in situations where it is not practical to place humans. The design of inspection robots, however, poses several challenges. Robots require intelligence, mobility and power, and must not pose any safety hazards, such as falling on, pinching, crushing or electrically shocking people and/or the structure. Previously reported robotic inspection systems include “The Robotic Inspector” (ROBIN), which is highly mobile and versatile, but is restricted by limited payload areas and a power cord [2], and robots that crawl through pipes that remain intact after a building collapse, search for survivors trapped in the wreckage and detect gas leaks [3].

Figure 1 Clamp-on Z-gage

Robots can operate with different levels of intelligence and control. Fully autonomous control is where the robot is turned loose on a structure and it has sufficiently preprogrammed intelligence to inspect the structure without human intervention. Semi-autonomous control is where the robot has sufficient internal intelligence for mobility and operation of internal systems, but is controlled at a high level by a human. Teleoperation is where a human is directly in the control loops. The advantages of teleoperation are that it can use human judgment in real time and can eliminate the need for an intelligent machine. However, this requires the real time transmission of sensor and control signals over high-bandwidth communication channels. Managing power supply and consumption is critical. Batteries or fuelconsuming power sources, as well as ambient energy harvesting, such as solar power, are possible for powering the robots and sensors. Efficient drive and electronics can significantly extend the range and sensing capabilities. Robotic systems need to be sufficiently mobile to move to points of interest. Building a single all-purpose mobile sensing robot is difficult due to the complex and varied geometries present in structures. The difficulty is reduced (along with the utility) if the robot is specialized to move over specific structures and to take specific measurements. FOUR GENERATIONS OF BEAM CRAWLING ROBOTS In an effort to examine and demonstrate some of the issues involved in the robotic inspection of structures, a series of robotic inspectors were designed and built with the narrowly-defined task of crawling along a structure on a predetermined path and taking measurements as required. To this end four generations of robots of increasing complexity and performance capabilities were built for the specific case of crawling along highway bridge beams and girders. Generation 1 Lego™ Robot An example of an early inspection robot appears in Figure 2. This robot was built out of Lego™ parts. The robot could move to particular locations by following a track with a vision sensor and to inductively power and interrogate an ASM (MicroStrain, Inc., Williston, VT) sensor system by a wireless connection. Preliminary tests of this concept were carried out on a platform with two thermisters attached to the ASM. The robot followed a path of black tape to the location of the ASM, paused, took readings, transmitted the data to a receiver. The robot paused and temperature data was transmitted to the receiver. Figure 3 shows the temperature change that was detected with the system in a cold liquid container warming up due to ambient temperature.

Figure 2 Lego™ robot with ASM (Microstrain, Inc.) inductive sensor coil

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Figure 3 Sample data of temperature change taken by Generation 1 robot Generation 2 Laboratory Beam Crawler The next generation robot was one that was built to crawl along a beam in the laboratory, locate ASM sensor modules, inductively power the modules, take data and transmit the data back to a central data logger/processor. The robot is controlled with a Z-World Jackrabbit BL1800 programmable microcontroller [4]. Inputs to the microcontroller include various touch sensors that are triggered when the robot finds an embedded sensor, reaches the end of a beam, and when the robot has returned to the docking station. Figure 4 shows the system. Figure 5 shows sample strain data that was taken and then transmitted as the beam was loaded. A design alternative that was considered but never implemented was to use a base docking station to replenish the power supply on the robot, download data, and program modified inspection protocols, as necessary. Practical implementation issues seem to lean against the utility of a base docking station for most robotic inspection systems. Generation 3 Field Deployed Beam Crawler The next step was to design a beam crawling robot for an in-service bridge. The bridge of interest was a multispan steel girder composite deck bridge on US Rte. 7

Inductive Sensor Power and Data Transmission Coils

Figure 4 Beam crawling robot in the laboratory

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Figure 5 Strain gage data collected by beam crawling robot

across the LaPlatte River in Shelburne VT. Although this bridge had a simple girder design with readily accessible girder beams, crawling along the flanges of the girders posed difficult challenges in terms of minimal clearance obstructions by intergirder diaphragms and variable flange thicknesses along the span. After consideration of a variety of mobility options, a rolling system with small wheels that could pass through the diaphragm obstructions was designed, built and tested. The crawler was driven down the beam with the chassis and drive train of a lowend remote control toy truck. This drive system ultimately proved to be underpowered and difficult to control with precision.

Generation 4 Upgraded Field Deployed Beam Crawler The Generation 3 beam crawler was upgraded with a better drive train that consisted of the chassis of a high end RC toy truck. The RC controls were modified to include autonomous control by an onboard Jackrabbit microprocessor, with a remotely switchable autonomous or semi-autonomous mode of operation. The robot was also equipped with a video camera that transmitted video data back to a host computer by a 2.4 GHz telemetry link. Figure 6 shows the robot mounted on the LaPlatte River Bridge and the chassis. Figure 7 shows the detection of a small dent in the girder due to preconstruction transportation. Figure 8 shows a system currently under development where the robot places and clamps a Z-gage to the structure at a specified location.

Figure 6 Beam crawling robot on LaPlatte River, VT Bridge and chassis.

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Figure 7 Small dent detected on girder of LaPlatte River, VT Bridge with robotmounted video camera.

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Z-Gage Held in non-sliding contact with flange using servo driven leadscrews Robot Chassis

Figure 8 Attachment of clamp-on Z-gage with robot

FUTURE ROBOTIC AND MOBILE SENSOR SYSTEMS The use of adaptive robotic and other mobile sensor systems shows the possibility for considerable development. This includes the increased miniaturization and performance of sensors that will enable the deployment of NDE instruments such as eddy current, ultrasound, impact echo, Schmidt hammer, and chemical assay; and the use of imaging systems with sensitivity over a wide spectral range placed onboard. Improved mobility is a key issue. This includes the development of specialized crawlers for structures with specific geometries, more agile crawlers, such as with gecko foot pads [5], and robots that can fly and/or swim. Figure 9 shows a hypothetical high-performance inspection robot. Articulated Camera, Microscope, Mass Spectrometer Adaptive/Morphing Body for Crawling, Flying and Swimming Steerable Antenna

Double Wings for Hover, Forward, and Reverse Flight

Autonomous and Controllable Solar Panels Gripper Feet, with Magnetic Claws and Gecko Pads

Figure 9 Hypothetical high-performance inspection robot

CONCLUSION Mobility and adaptability can significantly enhance and augment the performance of a structural sensing system. Robotic and mobile sensors offer the opportunity of adaptability without placing humans in dangerous and awkward situations. ACKNOWLEDGMENTS This work was assisted by Steve Arms and Jacob Galbreath of Microstrain, Inc., Williston, VT and by Tim Ambrose. Access to the LaPlatte River Bridge was provided by JB McCarthy of the Vermont Agency of Transportation. Funding for was provided in part by an NSF Phase II SBIR grant with Microstrain, Inc. REFERENCES [1] Arms, S.W., D.C. Guzik, C.P. Townsend. (1998) “Microminiature HighResolution Linear Displacement Sensor for Peak Strain Detection in Smart Structures,” Proc. SPIE Smart Structures and Materials, Vol. 3330, p.30-35. [2] Pack, R.T., M.Z. Iskarous, and K. Kawamura. 1996. “Climber Robot,” U.S. Patent # 5,551,525. [3] Nadis, S.. 2000. “Robot to the Rescue,” Popular Science, April 2000, p. 25. [4] Esser B., N. Pelczarski, D. Huston, and S. Arms. 2000. “Wireless Inductive Robotic Inspection of Structures,” Proc. IASTED, RA 2000, Honolulu, HI. [5] Geim A.K., S.V. Dubonis, I.V. Grigorieva, K.S. Novoselov, A.A. Zhukov, and S.Y. Shapoval. 2003. “Microfabricated Adhesive Mimicking Gecko Foot-Hair,” Nature Materials, Vol. 2.