Fast and precise motion control is important for industrial robots in manufacturing applications. However, some collaborative robots sacrifice precision for safety, particular for high motion speed. The performance degradation is caused by the inability of the joint servo controller to address the uncertain nonlinear dynamics of the robot arm, e.g., due to joint flexibility. We consider two approaches to improve the trajectory tracking performance through feedforward compensation. The first approach uses iterative learning control, with the gradient-based iterative update generated from the robot forward dynamics model. The second approach uses dynamic inversion to directly compensate for the robot forward dynamics. If the forward dynamics is strictly proper or is nonminimum-phase (e.g., due to time delays), its stable inverse would be non-causal. Both approaches require robot dynamical models. This paper presents results of using recurrent neural networks (RNNs) to approximate these dynamical modelsforward dynamics in the first case, inverse dynamics (possibly non-causal) in the second case. We use the bi-directional RNN to capture the noncausality. The RNNs are trained based on a collection of commanded trajectories and the actual robot responses. We use a Baxter robot to evaluate the two approaches. The Baxter robot exhibits significant joint flexibility due to the series-elastic joint actuators. Both approaches achieve sizable improvement over the uncompensated robot motion, for both random joint trajectories and Cartesian motion. The inverse dynamics method is particularly attractive as it may be used to more accurately track a user input as in teleoperation.
We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. To overcome this limitation, a novel 2D-based image processing method for tracking intra-fraction respiratory motion is proposed. Fifty-seven different anatomical features acquired from 27 sets of 2D ultrasound sequences were used in this study. Three 2D ultrasound sequences were acquired with the robotic ultrasound system from three healthy volunteers. The remaining datasets were provided by the 2015 MICCAI Challenge on Liver Ultrasound Tracking. All datasets were preprocessed to extract the feature point, and a patientspecific motion pattern was extracted by principal component analysis and slow feature analysis (SFA). The tracking finds the most similar frame (or indexed frame) by a k-dimensional-tree-based nearest neighbor search for estimating the tracked object location. A template image was updated dynamically through the indexed frame to perform a fast template matching (TM) within a learned smaller search region on the incoming frame. The mean tracking error between manually annotated landmarks and the location extracted from the indexed training frame is 1.80 ± 1.42 mm. Adding a fast TM procedure within a small search region reduces the mean tracking error to 1.14 ± 1.16 mm. The tracking time per frame is 15 ms, which is well below the frame acquisition time. Furthermore, the anatomical reproducibility was measured by analyzing the location's anatomical landmark relative to the probe; the position-controlled probe has better reproducibility and yields a smaller mean error across all three volunteer cases, compared to the force-controlled probe (2.69 versus 11.20 mm in the superior-inferior direction and 1.19 versus 8.21 mm in the anterior-posterior direction). Our method reduces the processing time for tracking respiratory motion significantly, which can reduce the delivery uncertainty.
We present a robotic system for transrectal ultrasound-guided prostate brachytherapy that employs a quick release mechanism to enable multiple needles to be inserted into the prostate prior to plan optimization. The mechanism consists of two actuated fingers that act as needle guides, thereby allowing insertion of both parallel and angled needles. Path planning, including reordering of needles within a batch, is required to avoid collisions with previously inserted needles. We perform two phantom experiments using clinical implant plans. The extra time required for the robotic motions, including finger actuation, is less than three minutes for the entire procedure. Mean position error is measured to be less than 0.5 mm, presumably due to the design of the needle guides, which have a toroidal shape to enable needle angulation.
This paper presents the design and initial results of a project involving the robotic assembly of a large segmented structure. This project aims to develop an operator-guided semi-autonomous assembly process using industrial robots integrated with multiple sensors. The goal is to demonstrate the potential of robotic technology to reduce cycle time, enhance assembly quality, and improve worker ergonomics, as compared to the current manual or fixture-based approaches. The focus is primarily on the software framework which is composed of a collection of commercial and customized components for robot positioning, motion planning, low latency teleoperation, visualization and simulation. A foundation step of the implementation is safe teleoperation which allows the user to operate the robot without concern of collision or joint limits. The concept has been demonstrated in RobotStudio, the simulation environment for ABB robots, and a physical ABB robot. While some of the software is specific to the ABB industrial robot used in the project, the framework is readily adapted to other industrial robots that allow externally commanded motion.
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