CHARACTERISATION AND TRACKING OF MEMBRANE SURFACES AT NASA LANGLEY RESEARCH CENTER Mark R. Shortis1, Stuart Robson2, Richard S. Pappa3, Thomas W. Jones4 and William K. Goad4 1
Department of Geomatics, University of Melbourne, Parkville, Australia,
[email protected] 2 Department of Geomatic Engineering, University College London, United Kingdom 3 Structural Dynamics Branch, NASA Langley Research Center, Hampton, Virginia, U.S.A. 4 Instrumentation Systems Development Branch, NASA Langley Research Center, Hampton, Virginia, U.S.A. KEYWORDS: surface characterisation, target tracking, membrane surface ABSTRACT This paper describes in detail two applications of characterisation and tracking of membrane surfaces using artificial targets. The first application discussed is the measurement of the membrane and spar wing surfaces of a micro-flight vehicle. The second application discussed is the measurement of a one metre long Fresnel lens membrane used to concentrate light on solar collectors. In both cases the aim was to investigate the structural dynamic characteristics of the surfaces under induced vibration. 1. INTRODUCTION NASA Langley Research Center (LaRC) in Virginia is one of several NASA research centres in the United States of America. The research programs at LaRC specialise in aerospace technology and atmospheric physics. Two of the principal experimental programs at LaRC are the design and analysis of aerospace structures for space deployment and the design and analysis of aerospace models for civilian and military organisations. Consequently there is a continual need for non-intrusive, high data-rate measurement for laboratory testing of aerospace structures and wind tunnel testing of aerospace models. Close range photogrammetry has been used routinely at LaRC as a measurement and tracking tool, using a wide variety of systems based both on film (Shortis, 1989) and CCD video (Shortis and Snow, 1997) cameras. The characterisation and tracking of surfaces at LaRC has typically been based on discrete targets to signalise points of interest or define the surface to be measured. Passive, internally illuminated and retro-reflective targets have all been used to accurately define surface points in generally unfavourable circumstances. For wind tunnel applications in particular, the lack of control over ambient lighting is always a factor (Childers et al, 1994; Shortis and Snow, 1997), however the primary reason for artificial targets is model
surfaces lacking in any clearly defined features because they are thin membranes or polished metal surfaces (Burner and Martinson, 1996; Graves and Burner, 2001). Further, artificial targets are favoured for tracking applications to ensure a high level of accuracy of the motion or change of shape (Robson and Shortis, 1997). The absolute shape of the surface, if required, may be derived from an initial, static test. The emphasis is typically on the accuracy of the relative changes in the surface to be tracked, in order to determine modes of vibration or cyclic motion. Although it is feasible to track natural surface features if they are available, it is generally accepted that artificial circular targets will realise superior accuracy. This paper describes two cases of the surface characterisation and tracking of membrane surfaces using artificial targets. In both cases the aim was to investigate the structural dynamic characteristics of the surfaces under induced vibration. A secondary issue for one case was a comparison between membranes of differing thickness. The targets were tracked using synchronised CCD video cameras and offline processing of the captured images. The results of the photogrammetric measurement were three dimensional visualisations of the trajectories of the surface targets. The paper will describe the experimental set-ups, the photogrammetric
geometry and image quality factors, and the algorithms used to track the target images. 2. WING SURFACES OF MICRO-FLIGHT VEHICLES The first application of photogrammetric monitoring of membrane surfaces is the measurement of the membrane and spar wing surfaces of a micro-flight vehicle. These 250 mm wing span vehicles are in the first stages of research and development towards a surveillance role in military engagements and civilian operations, as well as the subject of research to advance aerospace components and materials. The military aspect of the research and development is primarily in response to strong support from the Defense Advanced Research Projects Agency (DARPA) to develop micro aerial vehicles with a wing span of less than six inches and a speed of less than 25 miles per hour. Various types of wings are under investigation, however weight considerations have generally directed aerospace designers to transparent monofilm membrane surfaces supported by graphite/epoxy spars or battens. The concept of the micro-flight vehicle is that it will be an autonomous vehicle with a payload of a sensor and radio transmitter. The sensor is most likely to be a vision system with sensitivity in the visible or infra-red bands, however sensors for radio communications, radiation counters or biological weapons detectors are also likely. The vehicles will be released onto the battlefield to return intelligence information on the adversary. The perceived advantages of the vehicles are that they are not easily detected, relatively inexpensive and therefore expendable, yet capable of providing accurate and valuable information from redundant sources. Micro-flight vehicles also have civilian applications to search and rescue or fire fighting operations, for example. Previous research on micro-flight vehicles has concentrated on fixed wing designs with testing carried out in wind tunnels (Waszak et al, 2001). Both qualitative (flow visualisation) and quantitative (target tracking from a single, high frame rate camera) videometric techniques have been used to characterise the deformation of the membrane surfaces under wind load and at different angles of attack (Waszak et al, 2001). The new concept under development at Langley is a flapping
wing type vehicle with a self-contained power source, avoiding the noise or exhaust trail associated with conventional propulsion systems. The flapping of the wings is generated by a simple, low speed electric motor with an off-centre counterweight to induce flapping indirectly. The measurement task for the micro-flight vehicle prototype was to characterise the motion of the wing surfaces at different flapping frequencies and to compare two membranes of differing thickness. The set-up to capture imagery is shown in figure 1. The wings and flap motor are mounted vertically on an optical table, opposite two Hitachi monochrome CCD cameras, also mounted vertically. The configuration was later changed to a horizontal mounting of the cameras to improve the sensitivity of the tracking. The Hitachi cameras generate RS170 analog video output with a resolution of 752 by 480 pixels, captured in this case by dual Epix frame grabber cards. The cameras were locked together using a master-slave link through external synchronisation from one camera to the other. Ring lights were used to illuminate the retro-targets, placed both on the fixture, to provide a fixed reference, and on the spars of the wings, to determine the shape of the wings. Retro-targets were not placed on the membrane sections as the material is very delicate and because the spars control the overall shape of the surface. The camera set-up was calibrated using the small 3D target array seen lying on the optical table in figure 1. This fixture was moved around within the field of view of the cameras to simulate a multiexposure, convergent photogrammetric network. The network for the simultaneous calibration of the two cameras comprised 44 targets and 40 full frame exposures. The relative precision of the network was 1:32,000, corresponding to a mean coordinate precision of several micrometres for the targets. The camera calibration and the relative orientation of the two cameras were derived from the network. This technique uses post-processing of the camera station data to determine the base vectors and relative rotations of the two or more cameras, and has been used successfully for underwater stereo-video (Harvey and Shortis, 1996) and wind tunnel testing (Shortis and Snow, 1997) applications.
high frequencies the ring-lights were replaced with a single, centrally mounted strobe light. The strobe frequency was set to produce a full range of the wing movement over 1-2 seconds. The longest sequences of images captured were 15 seconds in duration, producing several hundred epochs of target tracking.
Figure 2. Left (top) and right images of the wing surfaces of the micro-flight vehicle.
Figure 1. Initial experimental set-up for the calibration and measurement of the wing surfaces of the micro-flight vehicle. Fields, rather than frames, were captured during the measurement process to increase the sample rate to 60Hz. The calibration data sets for the cameras were converted from frames, captured during the calibration process, to fields, captured during the measurement process, using the known relationship between frames and odd/even fields (Shortis and Snow, 1995). The use of fields, rather than frames, halves the vertical resolution of the exposures. However the aspect ratio of the object allowed the CCD sensors to be aligned with the horizontal axes parallel to the general direction of motion within the images, thereby minimising any loss in system sensitivity. Left and right images were captured as a sequence of individual images in TIFF format, and correlated using VITC time code generator input, as shown on the two images in figure 2. A number of sequences were captured at different wing flap frequencies. At
Figure 3. Visualisation of the target movement for the wing surfaces of the micro-flight vehicle. Coordinates for the targets for each epoch of measurement were computed from simple intersections. The estimated mean target coordinate precisions were approximately 20 micrometres. Target locations were tracked in object space using a three dimensional trajectory model. The predicted position in any new epoch was based on the previous three epochs of measurement to allow a non-linear extrapolation. The new object space position was then used to locate the target centroid window within the left and right images. Target coordinate data from the sequences were used to produce visualisations of the cyclic movement and
deflection of the wing surfaces (Woodhouse et al, 1999). An example is shown in figure 3. 3. SOLAR COLLECTOR FRESNEL LENS The second application discussed is the measurement of a one metre long Fresnel lens membrane used to concentrate light on solar collectors (Pappa et al, 2002). The lens is composed of silicone-rubber and is stretched between the end support arches (see inset figure 4). The lens produces a thin line of light directed at the centre of a rectangular solar collector in order to improve the efficiency of the energy conversion and reduce the overall weight.
The experimental set-up to characterise the surface shape and vibration modes of the Fresnel lens is shown in figure 5. The lens is mounted vertically with the base connected to an exciter unit used to simulate the vibrations from the shuttle reaction control system for in-orbit manoeuvres. Two small, circuit board CCD cameras were mounted on an independent fixture in order to image the inside surface of the lens through two viewing slots machined into the acrylic base of the lens element. This configuration was adopted to test the feasibility of in-flight monitoring of a lens array element that would be manufactured without the solar cells.
Figure 4. Artist impression of the deployed solar lens array – inset is a section of an assembled bank of solar cells and concentrator lenses. The 250 mm high lens elements will be assembled in banks (see inset figure 4) of 35 on panels with a dimension of three metres by one metre. The panels support the flexible concentrator lenses and the solar cells, and also serve as heat radiators. The overall weight of the solar lens array is just 1.6 kilograms per square metre and requires only 12% of the area of conventional solar cells for the same power output. A self-deployed structure composed of a series of hinged panels and containing 280 of the solar collectors may be tested in the cargo bay of the space shuttle during a future mission (figure 4). Once deployed, the solar lens array will be capable of 360 degrees of rotation to track the sun.
Figure 5. Experimental set-up for the surface measurement and tracking of the Fresnel lens – inset is detail of the top camera and viewing slot. The two CCD cameras produced RS-170 monochrome, analog video which was captured by a pair of Epix frame grabbers. The cameras were once more synchronised as a master-slave pair and frames were correlated using injected VITC time code. Passive targets were used throughout to avoid
reflections off the lens surface. The cameras were calibrated in a similar fashion to the micro-flight vehicle, using a small step-block target array within the fields of view to create a convergent multistation network. In this case a network of 10 exposures of the 36 targets was sufficient to calibrate the cameras and derive the relative orientation. The coordinates of the targets on the step-block had been previously determined with a precision of ten micrometres from a self-calibration network imaged with a Kodak DC4800 digital still camera.
Figure 6. Top and bottom images of the Fresnel lens.
Figure 7. Visualisation of the target movement of the Fresnel lens. Image pairs of the static lens and a number of sequences of the lens under induced vibration were captured as TIFF format images. As all the vibration periods were approximately 0.5 seconds or longer, full frames were captured at 30 Hz. Target coordinates were once more computed from simple intersections, in this instance with an estimated precision of 40 micrometres. An example pair of images of the lens is shown in figure 6 and an example of the visualisation of the motion of the surface targets is shown in figure 7.
4. TARGET TRACKING ISSUES The images shown in figures 2 and 6 demonstrate a number of typical issues associated with tracking targets on small objects or within constrained environments. The convergence of the cameras, used to enhance the object space accuracy or forced by the physical set-up, causes significant fall off in retro-reflector response or the intensity of the passive targets. Variations in background intensity are also present, due to reflections off the membrane surfaces and ambient light sources. The effects of variations in target and background intensity were minimised by using a local threshold within the target image window for the centroid computation. The convergence of the cameras also causes a falloff in the size and spacing of the targets across the objects, with the Fresnel lens showing a particularly extreme size variation. This effect was partially ameliorated using a two level adaptive window for the target image centroids. First, the initial size of the window for each target was computed based on the relative depth of the target with respect to the imaging camera. Second, the algorithm progressively shrinks the window for each target image centroid if intrusions into the edge of the window are detected. Also, the targets are processed in depth order from the imaging camera, assuming that any nearer targets will be in the foreground and may obscure more distant targets that are in the background. However, there are a number of issues that require additional sophistication in procedures or algorithms to minimise the effects on the accuracy and reliability of target tracking. For example, variations in intensity are also seen on the large passive targets in the foreground for the Fresnel lens. The movement of the lens introduced a cyclic bias in these variations, leading to systematic errors in the target locations. The only immediate remedy for this bias would be more careful attention to lighting and image quality on a case by case basis. Perhaps the most challenging aspect of tracking applications is the “loss of lock” problem. Target images that are obscured, merge or fail to produce an acceptable centroid due to reflections, low intensity or marginal size, are not intersected and therefore are not included in the tracking process. Despite the use of the adaptive window and object space motion prediction, loss of lock on targets
remained a regular problem, requiring operator intervention in an otherwise automated process. An enhancement to the tracking process that reduces the number of target losses is the use of a Delaunay triangulation (see figure 3). Such triangle meshes are often used as a surface descriptor or as a mechanism to densify surface points (Papadaki et al, 2001), whereas here the common object and image space connectivity between points in the mesh is used as a reliability test. Established in the initial, static epoch of measurement, the mesh simply provides a consistent description of the spatial relationships between targets that is independent of the induced vibration modes. The triangulation can be used for a number of tracking assistance purposes. Given any loss of lock on, or the mis-identification of, an individual surface target, the connectivity within the mesh can be consulted in the form of a simple look up table to resolve most ambiguities. The winding of the triangles forming the Delaunay mesh can be used to validate computed lines of sight to targets and detect if predicted positions are such that two targets will overlap or if a target will be obscured by the surface. The structure of the mesh can also be used to predict when the target will reappear and thereby resume lock on the target. This research is still in the formative stages, however, re-processing of the Fresnel lens image sequences has indicated that the technique reduces the frequency of loss of lock on targets during the tracking. 5. CONCLUSIONS This paper has presented two cases of characterisation and tracking of membrane surfaces at NASA Langley Research Center. The effectiveness and versatility of photogrammetric techniques to provide precise and reliable information on dynamic surfaces has been demonstrated. Notwithstanding this, further improvement in the reliability of target tracking is possible using techniques such as the interpretation of a surface mesh. 6. REFERENCES Burner, A. W. and Martinson, S. D., 1996. Automated wing twist and bending measurements under dynamic load. AIAA 1996-2253, 19th AIAA Advanced Measurement and Ground Testing Technology Conference, New Orleans, USA.
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