AbstractMost of industrial robots are still programmed using the typical teaching process, through the use of the robot teach pendant. In this paper is proposed an accelerometer-based system to control an industrial robot using two low-cost and small 3-axis wireless accelerometers. These accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An Artificial Neural Network (ANN) trained with a back-propagation algorithm was used to recognize arm gestures and postures, which then will be used as input in the control of the robot. The aim is that the robot starts themovement almost at the same time as the user starts to perform a gesture or posture (low response time). The results show that the system allows the control of an industrial robot in an intuitive way. However, the achieved recognition rate of gestures and postures (92%) should be improved in future, keeping the compromise with the system response time (160 milliseconds). Finally, the results of some tests performed with an industrial robot are presented and discussed.

INTRODUCTION

ROGRAMMING and control an industrial robot through the use of the robot teach pendant is still a tedious and time-consuming task that requires technical expertise. Therefore, new and more intuitive ways for robot programming and control are required. The goal is to develop methodologies that help users to control and program a robot, with a high-level of abstraction from the robot specific language. Making a robotic demonstration in terms of high-level behaviors (using gestures, speech, manual/human guidance, from visual observation of human performance, etc.), the user can demonstrate to the robot what it should do [1]-[5]. In the robotics field, several research efforts have been directed towards recognizing human gestures, recurring to vision-based systems [6], [7], motion capture sensors [2], [4], or using finger gesture recognition systems based on active tracking mechanisms [8]. Accelerometer-based gesture recognition has become increasingly popular over the last decade. The low-moderate cost and relative small size of the accelerometers make it an effective tool to detect and recognize human body gestures. Several studies have been conducted on the recognition of gestures from acceleration data using Artificial Neural Networks (ANNs) [9], [10], [11]. However, the specific characteristics of an industrial environment (colors, non-controlled sources of light, infrared radiation, etc.), the safety and reliability requirements, and the high price of some equipment has hampered the deployment of such systems in industry. Given the above, the teach pendant continues to be the common robot input device that gives access to all functionalities provided by the robot (jog the manipulator, produce and edit programs, etc.). In the last few years the

INTRODUCTION contd..

robot manufacturers have made great efforts to make user-friendly teach pendants, implementing intuitive user interfaces such as icon-based programming [12], color touch screens, a 3D joystick (ABB Robotics), a 6D mouse (KUKA Robot Group) [13], or developing a wireless teach pendant (COMAU Robotics). Nevertheless, it remains difficult and tedious to operate with a robot teach pendant, especially for non-expert users. In this paper is proposed an accelerometer-based gesture recognition system to control an industrial robot in a natural way. Two 3-axis wireless accelerometers are attached to the human arms, capturing its behavior (gestures and postures). An ANN system trained with a back-propagation algorithm was used to recognize gestures and postures. Finally, several tests are done to evaluate the proposed system. The results of the performed tests are presented and discussed.

SYSTEM OVERVIEW

A. System Description The demonstration cell (Fig. 1) is composed of an industrial robot MOTOMAN HP6 equipped with the NX100 controller, two 3-axis wireless accelerometers to capture human hand behaviors, and a computer running the application that manages the cell. The 3-axis accelerometers (ADXL330, Analog Devices) are physically rated to measure accelerations over a range of at least +/- 3g, with a sensitivity of 300 mV/g and sensitivity accuracy of 10%. The accelerometers communicate with the computer via Bluetooth wireless link, reporting back data at 100 Hz (Fig. 2).

B. Methodology The 3-axis accelerometer attached to the right arm is used to recognize gestures (dynamic arm positions) and postures (static arm positions), whereas the accelerometer attached to the left arm recognizes the postures used to activate and deactivate the system (only two postures). In practice, the user should make a gesture with the right arm and at the same time use the left arm to activate or deactivate the system. When activated, the system acquires data from the accelerometer attached to the right arm, recognizes the gesture or posture and starts the robot movement. Performing a specific posture with the left arm, the robot stops. If the user never stops the robot, the robot continues the movement up to the limit of its field of operation. An ANN system trained with a back-propagation algorithm was used to recognize gestures and postures. The ANN system has as input the motion data (extracted from the accelerometer attached to the right arm) and as output the recognized gestures and postures. The application that manages the cell receives data from the accelerometers, interprets the received data and acts in

the robot, using for this purpose the MotomanLib, a Data Link Library created in our laboratory to control and manage the robot remotely via Ethernet (Fig. 3). Given that the accelerometers communicate with the computer via Bluetooth, it is important to take into account the reliability of this type of communication and use it with care. The system here presented is continuously receiving data from the accelerometer attached to the left arm and if the communication fails, the robot immediately stops.

CONTROL STRATEGY A. Robot Control The robot is controlled remotely via the Ethernet using a command that moves the robot linearly according to a specified pose increment [ ] T6 5 4 3 2 1 i i i i i i i =. The first three components represent the robot translation along the X, Y and Z axes, respectively, whereas the last three components represent the robot rotation about the X, Y and Z axes, respectively. These components i have the necessary information to control the robot. It is therefore necessary to identify them by examining the behavior of the user right arm. In this system it is completely unnecessary to extract precise displacements or rotations, being only required to know which of the pose increment components must be activated. In a first approach, the robot control strategy was to identify translation movements and rotations of the user hand and, depending on these inputs, small pose increments were continuously sent to the robot. However, it was quickly concluded that this approach was not viable because the robot was constantly halting, presenting a high-level of vibration. The achieved solution was to send to the robot only one pose increment that will move the robot to the limit of the field of operation.

GESTURE AND POSTURE RECOGNITION

Mode of operationThe robot moves along the X, Y and Z axes separately (robot translations). The rotation around each of the three axes is also done separately, an axis at a time. When the accelerometer is operating in a dynamic way, the gravity components will appear mixed with the inertial components of acceleration. In order to prevent this situation, when the user makes gestures, the accelerometer must be kept horizontal (Fig. 5). Thus, it is known that the force of gravity acts along the Z axis. For example, to move the robot in the X direction, the user should move the accelerometer along the X axis, keeping it in the horizontal. Of course it is humanly impossible to keep the accelerometer exactly in the horizontal, but this is a way to have some control over the process. Thus it is relatively easier to control the acceleration due to gravity and recognize gestures.

GENERAL DESCRIPTION The ADXL335 is a small, thin, low power, complete 3-axis accelerometer with signal conditioned voltage outputs. The product measures acceleration with a minimum full-scale range of ±3 g. It can measure the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion, shock, or vibration. The user selects the bandwidth of the accelerometer using the CX, CY, and CZ capacitors at the XOUT, YOUT, and ZOUT pins. Bandwidths can be selected to suit the application, with a range of 0.5 Hz to 1600 Hz for the X and Y axes, and a range of 0.5 Hz to 550 Hz for the Z axis. The ADXL335 is available in a small, low profile, 4 mm × 4 mm × 1.45 mm,16-lead, plastic lead frame chip scale package (LFCSP_LQ).

The ADXL335 is a complete 3-axis acceleration measurement system. The ADXL335 has a measurement range of ±3 g minimum. It contains a polysilicon surface-micro machined sensor and signal conditioning circuitry to implement open-loop acceleration measurement architecture. The output signals are analog voltages that are proportional to acceleration. The accelerometer can measure the static acceleration of gravity in tilt-sensing applications as well as dynamic acceleration resulting from motion, shock, or vibration. The sensor is a polysilicon surface-micro machined structure built on top of a silicon wafer. Polysilicon springs suspend the structure over the surface of the wafer and provide a resistance against acceleration forces. Deflection of the structure is measured using a differential capacitor that consists of independent fixed plates and plates attached to the moving mass. The fixed plates are driven by 180° out-of-phase square waves. Acceleration deflects the moving mass and unbalances the differential capacitor resulting in a sensor output whose amplitude is proportional to acceleration. Phase-sensitive demodulation techniques are then used to determine the magnitude and direction of the acceleration.The demodulator output is amplified and brought off-chip through a 32 kΩ resistor. The user then sets the signal bandwidth of the device by adding a capacitor. This filtering improves measurement resolution and helps prevent aliasing.

The ADXL335 uses a single structure for sensing the X, Y, and Z axes. As a result, the three axes’ sense directions are highly orthogonal and have little cross-axis sensitivity. Mechanical misalignment of the sensor die to the package is the chief source of cross-axis sensitivity. Mechanical misalignment can, of course, be calibrated out at the system level.

Rather than using additional temperature compensation circuitry, innovative design techniques ensure that high performance is built in to the ADXL335. As a result, there is no quantization error or nonmonotonic behavior, and temperature hysteresis is very low (typically less than 3 mg over the −25°C to +70°C (temperature range).

For most applications, a single 0.1 μF capacitor, CDC, placed close to the ADXL335 supply pins adequately decouples the accelerometer from noise on the power supply. However, in applications where noise is present at the 50 kHz internal clock frequency (or any harmonic thereof), additional care in power supply bypassing is required because this noise can cause errors in acceleration measurement. If additional decoupling is needed, a 100 Ω (or smaller) resistor or ferrite bead can be inserted in the supply line. Additionally, a larger bulk bypass capacitor (1 μF or greater) can be added in parallel to CDC. Ensure that the connection from the ADXL335 ground to the power supply ground

is low impedance because noise transmitted through ground has a similar effect to noise transmitted through V.

The ST pin controls the self-test feature. When this pin is set to VS, an electrostatic force is exerted on the accelerometer beam. The resulting movement of the beam allows the user to test if the accelerometer is functional. The typical change in output is −1.08 g (corresponding to −325 mV) in the X-axis, +1.08 g (or +325 mV) on the Y-axis, and +1.83 g (or +550 mV) on the Z-axis. This ST pin can be left open-circuit or connected to common (COM) in normal use. Never expose the ST pin to voltages greater than VS + 0.3 V If this cannot be guaranteed due to the system design (for instance, if there are multiple supply voltages), then a low VF clamping diode between ST and VS is recommended.

The selected accelerometer bandwidth ultimately determines the measurement resolution (smallest detectable acceleration). Filtering can be used to lower the noise floor to improve the resolution of the accelerometer. Resolution is dependent on the analog filter bandwidth at XOUT, YOUT, and ZOUT. The output of the ADXL335 has a typical bandwidth of greater than 500 Hz. The user must filter the signal at this point to limit aliasing errors. The analog bandwidth must be no more

than half the analog-to-digital sampling frequency to minimize aliasing. The analog bandwidth can be further decreased to reduce noise and improve resolution. The ADXL335 noise has the characteristics of white Gaussian noise, which contributes equally at all frequencies and is described in terms of μg/√Hz (the noise is proportional to the square root of the accelerometer bandwidth). The user should limit bandwidth to the lowest frequency needed by the application to maximize the resolution and dynamic range of the accelerometer. With the single-pole, roll-off characteristic, the typical noise of the ADXL335 is determined by )1.6( ×× = BW Density Noise Noiserms It is often useful to know the peak value of the noise. Peak-to-peak noise can only be estimated by statistical methods. Table 5is useful for estimating the probabilities of exceeding various peak values, given the rms value.

The ADXL335 is tested and specified at VS = 3 V; however, it can be powered with VS as low as 1.8 V or as high as 3.6 V. Note that some performance parameters change as the supply voltage is varied. The ADXL335 output is ratiometric, therefore, the output sensitivity (or scale factor) varies proportionally to the supply voltage. At VS = 3.6 V, the output sensitivity is typi- cally 360 mV/g. At VS = 2 V, the output sensitivity is typically 195 mV/g. The zero g bias output is also ratiometric, thus the zero g output is nominally equal to VS/2 at all supply voltages. The output noise is not ratiometric but is absolute in volts; therefore, the noise density decreases as the supply voltage increases. This is because the scale factor (mV/g) increases while the noise voltage remains constant. At VS = 3.6 V, the X-axis and Y-axis noise density is typically 120 μg/√Hz, whereas at VS = 2 V, the X-axis

and Y-axis noise density is typically 270 μg/√Hz. Self-test response in g is roughly proportional to the square of the supply voltage. However, when ratiometricity of sensitivity is factored in with supply voltage, the self-test response in volts is roughly proportional to the cube of the supply voltage. For example, at VS = 3.6 V, the self-test response for the ADXL335 is approximately −560 mV for the X-axis, +560 mV for the Y-axis, and +950 mV for the Z-axis. At VS = 2 V, the self-test response is approximately −96 mV for the X-axis, +96 mV for the Y-axis, and −163 mV for the Z-axis. The supply current decreases as the supply voltage decreases. Typical current consumption at VS = 3.6 V is 375 μA, and typical current consumption at VS = 2 V is 200 μA

CONCLUSION AND FUTURE WORK Due to the growing demand for natural Human Machine Interfaces and robot intuitive programming platforms, a robotic system that allows users to control an industrial robo using arm gestures and postures was proposed. Two 3-axis accelerometers were selected to be the input devices of this system, capturing the human arms behaviors. When compared with other common input devices, especially the teach pendant, this approach using accelerometers is more intuitive and easy to work, besides offering the possibility to control a robot by wireless means. Using this system, a non-expert robot programmer can control a robot quickly and in anatural way. The low price and short set-up time are other advantages of the system. Nevertheless, the reliability of thesystem is an important limitation to consider. The ANN’s shown to be a good choice to recognize gestures and postures, presenting an average of 92% of correctly recognized gestures and postures. The system response time (160 milliseconds) is another important factor. Future work will build upon the improvement of the average of correctly recognized gestures. One approach might be the implementation of a gyroscope into the system in order to separate the acceleration due to gravity from the inertial acceleration. The use of more accelerometersattached to the arms is another possibility.

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