Kinematic calibration of a robotic manipulator determines its fabricated dimensions that inevitably deviate, due to manufacturing tolerances, from the specified/designed geometry. Conventional calibration methods achieve this goal by deriving a system of constraint equations from the kinematic structure. Workspace coordinates and the corresponding joint space coordinates are experimentally obtained so that these constraint equations can be solved for the optimal geometric parameters that best fit the data. This paper proposes an alternative kinematic calibration method that leverages response surface methodology to obtain empirical models of direct and inverse kinematics from the same experimental data. The advantages the proposed method include nullifying the requirements of deriving constraint equations and performing joint sensor calibration. In addition, since the developed models are empirical in nature, whether closed-form solutions to the direct or the indirect model is available becomes conveniently inconsequential. Both physical and simulation experiments were conducted on a 3-PSS/S to evaluate the accuracy of the proposed method.

A high speed image based visual servoing (VS) technique is developed in this study for a Selective Compliant Assembly Robotic Arm (SCARA) manipulator with rotary hydraulic actuators. This study has developed a 2D position measuring system which comprise a high speed camera with a position sensitive detector as the image sensor. The input output interface and the controller for the VS system was implemented using a field programmable gate array (FPGA) providing a single chip solution for the embedded system. This camera was capable of providing position measurements of the end effector (EE) with an accuracy of up to 0.95 mm at a frequency of 1340 Hz. The proposed control strategy produced a better tracking performance with an EE payload of 12 kg with speeds of up to 1.3 m/s.

This paper details the development of a tracking controller for a highly dynamic parallel orientation manipulator that is capable of achieving high angular acceleration. The adopted control algorithm is derived from the active disturbance rejection control (ADRC) technology. The ADRC algorithm must be evaluated on the control hardware at a frequency consistent with the dynamics of the system without creating any inter-channel latency for a successful implementation. In this regard, the field programmable gate array (FPGA) is preferred because of its superior speed and parallelism. While there is a clear demand for FPGA controllers in the military and the aerospace domain because of the improved SWaP (size, weight, and power) capabilities, the literature provides a few examples of such controllers that mostly employ expensive high logic density FPGA chips. In contrast, this paper describes how an advanced controller can be efficiently prototyped on a relatively low-cost, low logic density FPGA hardware. The controller was tested in a co-simulation approach (wherein the digital implementation of the control algorithm and the dynamics of the manipulator are simulated together) to ensure a robust FPGA implementation. In addition, a hardware evaluation was also conducted using a linear voice coil actuator under varying inertial loads. Experimental results obtained from the simulation study and hardware testing confirm the robust performance of the designed controller.

This paper outlines a video-based method for detecting intruder aircraft to assist with sense and avoid for small, unmanned aerial vehicles (UAVs). A key consideration is that the algorithm is suitable for real-time implementation on field-programmable gate arrays (FPGAs). The method begins by estimating the motion in the scene using regional phase correlation, and then fitting the positional predictions obtained using these regional motion vectors to an affine model representing the effect of camera motion on the background imagery. A combination of metrics, including phase correlation peak height (a confidence measure) and the error between the position predicted by the affine model and that obtained using the measured phase correlation vector, is used to indicate regions of interest where moving targets are present. The ability of the algorithm to detect approaching aircraft is analyzed using a number of aerial video sequences with different encounter geometries.

Voice-coil actuators are the simplest form of electric motor consisting of a non-commutated single coil or winding moving through a fixed magnetic field produced by stationary permanent magnets. From a system design point of view, however, it is generally the end user's responsibility to couple the voice-coil actuator with a linear bearing system, position feedback device, switch-mode or linear servo amplifier, and motion controller. The integration of multiple discrete components adversely affects system reliability and renders minimization and packaging difficult particularly when multiple actuators are required. In response to this demand, a novel, low-inertia voice coil actuator has been developed whereby the traditional moving coil is replaced with a printed circuit board (PCB) that incorporates the necessary windings as conductive traces on one or more layers of the board. The result is a compact, highly integrated, highly reliable design that is simple to mass-produce using conventional PCB manufacturing and assembly techniques.

In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters that describe the image formation process for a given analytical model of the machine vision system. Researchers working with low-cost digital cameras and off-the-shelf lenses generally favor camera calibration techniques that do not rely on specialized optical equipment, modifications to the hardware, or an a priori knowledge of the vision system. Most of the commonly used calibration techniques are based on the observation of a single 3-D target or multiple planar (2-D) targets with a large number of control points. This paper presents a novel calibration technique that offers improved accuracy, robustness, and efficiency over a wide range of lens distortion. This technique operates by minimizing the error between the reconstructed image points and their experimentally determined counterparts in "distortion free" space. This facilitates the incorporation of the exact lens distortion model. In addition, expressing spatial orientation in terms of unit quaternions greatly enhances the proposed calibration solution by formulating a minimally redundant system of equations that is free of singularities. Extensive performance benchmarking consisting of both computer simulation and experiments confirmed higher accuracy in calibration regardless of the amount of lens distortion present in the optics of the camera. This paper also experimentally confirmed that a comprehensive lens distortion model including higher order radial and tangential distortion terms improves calibration accuracy.

This paper proposes using servo hydraulics for high speed robotic manipulation. It details the development of a novel double vane rotary type actuator custom designed for use in a Selective Compliant Assembly Robotic Arm (SCARA). The system, which is mathematically modeled and simulated, consists of an electro-hydraulic servo valve, double vane rotary actuator, manipulator and a controller. Based on the simulation results, hydraulic actuators were sized for optimal performance. A prototype of the proposed manipulator was built and tested. The test results show that the proposed actuator is capable of reaching torques of up to 860 Nm. The end effector of the manipulator is capable of reaching average velocities in excess of 2.7 ms-1with a payload capability of 5.3 kg. Comparable performance is not feasible with contemporary SCARA type robots with electric motors.

This study details the development of a high performance servo-hydraulic actuator for a Selective Compliant Assembly Robotic Arm (SCARA). The arm is intended for high speed food processing applications; specifically on-line poultry deboning. The system is mathematically modeled and simulated. Based on the simulation results, the hydraulic actuators are sized for optimal performance. A prototype actuator is subsequently designed, manufactured and experimentally evaluated. The tests results demonstrate that the prototype actuator is capable of producing unprecedented torques and associated accelerations relative to its size and mass. Comparable performance is not feasible with contemporary electrical actuators of similar size.

This paper presents an efficient, noncontact measurement technique for the automatic identification of the real kinematic parameters of an industrial robot. The technique is based on least-squares analysis and on the Hayati and Mirmirani kinematic modeling convention for closed kinematic chains. The measurement system consists of a single camera mounted on the robot's wrist. The camera measures position and orientation of a passive target in six degrees of freedom. Target position is evaluated by applying least-squares analysis on an overdetermined system of equations based on the quaternion representation of the finite rotation formula. To enhance the accuracy of the measurement, a variety of image processing functions including subpixel interpolation are applied.

The article presents a logic branching weighted algorithm (LBWA) to train a robot to perform splined shaft and hole assembly in a robotic cell. The LBWA uses angular and linear positional changes and assigns weights to each of these based on the force sensing information from an assembly path and evolves a best move strategy for the robot to complete the task. The machine learning capability of the robot depends on the discretization of the force-torque information that is monitored and mapped for each position. Prior to commencing the move, the LBWA compares the evaluating functions. A trade-off is to be made between the information space and the learning time for the robot in a real-life situation. Experimental results are presented to establish the effectiveness of the LBWA in training the robot.