Capacitive sensor for object ranging and material type identification

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Abstract

This paper presents a system for object ranging and material type identification using a multifrequency approach for a capacitive sensor. It is shown through an experimental study that the deviation in the readings taken at different sensor drive frequencies and the variation in consecutive readings provide sufficient information to distinguish a range of material types commonly found in a number of environments. A supervised learning scheme is used to classify the material type of planar patches. Extensive experimental evidence is presented to demonstrate the potential of the system. The capacitive based, object penetrating, material type identification is targeted for use with an autonomous robotic system for steel bridge maintenance; significantly different interaction is required for each of the various materials present. Experimental results demonstrate that the information from the sensor is sufficient to range and identify the material type (via physical properties) of an object present in a scene where a bridge structure is being grit-blasted.

Introduction

The use of sensors has become ubiquitous in simple and complex systems alike. They are often the component that facilitates the vital link between the physical world and the system. And in such, robust and reliable operation in a variety of environments is paramount. Typically it seems that sensors are designed with a specific scope in mind. Assumptions are made regarding the environmental conditions under which the sensor is to operate and of the accuracy required. Often, as sensing accuracy is the primary concern, the range of conditions under which the sensor can operate is limited. Technologies that perform moderately well in a larger range of environments are overlooked, with those performing well in typical environments (often lab conditions) being pursued.

This can be problematic, for instance, consider the case of a system designed to operate in construction/maintenance environments. The air may vary from clear to heavily laden with particles (both conductive and non-conductive), the audible noise levels will range from relative silence to extreme noise, debris will settle on the sensor and the chance of the sensor being subjected to an impact is high. As found in previous work [1], [2], typical sensing technologies are doomed to fail under such conditions. For instance, of the several mature sensing categories available for object ranging and material type identification: Laser, Ultrasonic, InfraRed (IR), Radio Frequency (RF), Visual and Capacitive, all face issues. The airborne particles disperse the laser, ultrasonic, RF and IR [3] and impair the vision sensor [4] rendering them unusable. Ultrasonic is further impaired by the white noise, as is IR by the presence of natural light.

Capacitive-based sensing offers many advantages. The broad distribution of the electric field allows a relatively large area of coverage (compared to the size of the sensor) with a single sensor. Additionally, capacitive sensors are insensitive to lighting, audible noise, or the color, shape, surface and texture of the object [5], [6]. Although many manufactures offer capacitive sensors designed for short distances (<1 cm) [7] and research tends towards very short range capacitive sensors (<2000 μm) [8] this is not indicative of the technologies capabilities as demonstrated by Novak and Feddema, who developed a capacitive sensor with a range of 40 cm [6]. Capacitive base material type identification has also proved possible [9]. Although, in the case of [9]; the required sensor-surface proximity and the sensor's dependency on the optical properties of the surface, make it unsuitable for use in construction/maintenance applications.

Airborne particles and the sensed object's material type will affect the accuracy of capacitive range sensors [8], [9], [10], [11]. However, due to the nature of the technology, the affect of the airborne particles is not prohibitive (as with the aforementioned technologies) and the effect of the object's material type is potentially measurable and addressable directly by the sensor. Even so, to the authors’ best knowledge, no work has been done to combine and adapt the capacitive technologies to provide a robust and reliable sensor for object ranging and material type identification in harsh environments; such as those found during the grit-blasting component of steel bridge maintenance.

Presented herein is the Adaptive Capacitive Sensor for Object Ranging (ACSOR): a multifrequency capacitive-based sensor designed for object ranging and object material type identification in both clear air and in air heavily laden with airborne particles (typical in construction and maintenance environments). The sensor has been experimentally verified via use for object ranging and high confidence blastable area detection (object material type identification) in a robotic grit-blasting system for steel bridge maintenance. The breakdown of this paper is as follows; firstly the fundamentals of the sensor will be discussed. This is followed by a description of the sensor's unique method of operation. Experimentally derived results will then be presented with conclusions drawn and future work proposed.

Section snippets

Fundamentals of capacitive sensing

Fig. 1 shows a schematic of the ACSOR's active sensing component. The ACSOR is built on the fundamental technology developed by Novak in [5]. The reader is referred to [5] for an in-depth explanation. For the sake of completeness a concise explanation will now be given.

Referring to Fig. 1, electrode 1 is connected to a drive oscillator and electrode 2 to a charge amplifier. As the magnitude of the electric field generated by the drive oscillator on electrode 1 is fixed and as electric fields

Development of hypotheses

As can be appreciated from (1), the permittivity, ɛ (a physical property) of an object in the sensing field will affect the maximum charge, Q, and thus the accuracy of the range measurement. Restated: the material type of the sensed object directly affects the accuracy of the sensor. Both Novak [5], [6] and Smith [11] addressed this limitation by assuming that the various objects being sensed will have similar permittivity. Whilst this assumption is reasonable in a number of instances we found

Active sensing and signal processing

To test this hypothesis a sensor was designed and manufactured to use as a test platform, shown in Fig. 2. The two aforementioned electrodes (Fig. 1) are clearly visible in the figure. The component side of the board contains a charge-to-voltage converting circuit and a micro-controller that both reads this voltage and is used to set the drive frequency. The sensor is physically robust.

Fig. 3 presents a block diagram of the sensor operation. The Active Sensing block consists of the

Measuring response functions

The first experiment was designed to quantify the sensor output when sensing objects of various different material types over a range of distances. This purpose of this experiment was to produce Response Functions (for use in ranging) and to gauge the likelihood that classification of the material type of a sensed object is possible for these materials.

Fig. 4 shows the experimental set-up, the ACSOR was fitted to the end-effector of a 6DOF anthropomorphic robotic arm. The robotic arm was

Conclusions

This paper presented the Adaptive Capacitive Sensor for Object Ranging and material type identification (ACSOR): a multifrequency capacitive-based sensor designed for robust, reliable and high-confidence object ranging and object material type identification in typical construction/maintenance environments.

Experiments have demonstrated the ACSOR's ability to robustly and reliably identify the material type group (concrete, human, wood or metal) of a sensed object based on the objects

Acknowledgments

This work is supported by the ARC Linkage Grant (ARC-LP0776312), by the ARC Centre of Excellence for Autonomous Systems (CAS), the Roads and Traffic Authority (RTA) and the University of Technology, Sydney.

Nathan Kirchner completed BEng in Mechatronics Engineering at the University of New South Wales, Sydney, in 2005 and is currently studying PhD in Mechatronics at the ARC Centre for Autonomous Systems at the University of Technology, Sydney. During his time as an undergraduate he published several journal and conference papers, one of which was awarded with a best paper award at the International Conference on Sensing Technology, Massey University, New Zealand, 2005. During his PhD studies he

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Nathan Kirchner completed BEng in Mechatronics Engineering at the University of New South Wales, Sydney, in 2005 and is currently studying PhD in Mechatronics at the ARC Centre for Autonomous Systems at the University of Technology, Sydney. During his time as an undergraduate he published several journal and conference papers, one of which was awarded with a best paper award at the International Conference on Sensing Technology, Massey University, New Zealand, 2005. During his PhD studies he has thus far received a university faculty of engineering award for “Best Innovation” and received the “Best Paper Award” at the 23rd International Symposium on Automation and Robotics in Construction, Waseda University, Tokyo, 2006, along with generating a provisional patent covering a portion of his work and publishing several papers.

Daniel Hordern is a final year BEng (Mechatronics) Capstone student at the University of Technology, Sydney (UTS). Throughout his undergraduate studies he has been the recipient of several prizes; including the highly competitive “John Heine Continuing Education Prize”, the “John Heine Memorial Prize” and twice the “AIM Product Prize”. He has also been a member of the Faculty of Engineering Dean's Merit List for the duration of his studies. He is currently an intern at the ARC Centre for Autonomous Systems at UTS.

Gamini ‘Dissa’ Dissanayake is the James N. Kirby Professor of Mechanical and Mechatronic Engineering at University of Technology, Sydney (UTS). He has expertise in a broad range of topics in robotics including, robot localization, mapping and simultaneous localization and mapping (SLAM) using sensors such as laser, radar, vision and inertial measurement units; terrain mapping; multi-robot coordination for SLAM, target tracking and probabilistic search; motion planning for single and multiple robot manipulators, legged robots, and cranes; and application of robotic systems in urban search and rescue. He leads the UTS node of the ARC Centre of Excellence for Autonomous Systems. He graduated in Mechanical/Production Engineering from the University of Peradeniya, Sri Lanka in 1977. He received his MSc in Machine Tool Technology and PhD in Mechanical Engineering (Robotics) from the University of Birmingham, England, in 1981 and 1985, respectively.

Dikai Liu is an Associate Professor at the ARC Centre of Excellence for Autonomous Systems, University of Technology, Sydney. He has expertise in intelligent mechatronic systems and computational intelligence, including intelligent robotics in automation of logistics and infrastructure maintenance; coordination and control of a large fleet of autonomous robots in various environments; path/motion planning and collision avoidance for mobile robot and robot manipulators in complex 3D environments; human–robot interaction and control; genetic algorithms and PSO and their applications. He has been developing intelligent systems to perform various tasks automatically and/or autonomously. Example systems include an autonomous robotic system for steel bridge maintenance; a robotic mobility assistant; an intelligent wheelchair; a multi-autonomous robot system for material handling; and high precision FRP filament winding machines.

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