Ultrasonic motor-induced geometric distortions in magnetic resonance images.

Ultrasonic motors (USMs) are common actuators that can be safely used in the magnetic resonance imaging (MRI) environment. However, lack of MRI compatibility results in issues such as image distortion. This fact led researchers to shift focus from USMs to pneumatic and hydraulic actuators in development of surgical robots. The aim is to quantify and compensate the geometric distortion of MR images as generated by the presence of USMs. An ultrasonic motor was positioned in three orientations with respect to the bore axis. The induced distortions were compared across four image sequences. To reduce the distortions, three artifact reduction methods were employed. Geometric distortion is the only artifact in image slices farther from the motor. The various motor orientations lead to different distortions, with the lowest distortion for the z orientation. The maximum measured distortion of ten pixels occurred. This maximal distortion is equal to a 1-cm displacement of the displayed points relative to their actual locations and it is beyond the acceptable level for medical display standards. Bandwidth reduction reduced the distortion, with a 50% reduction for a doubled bandwidth. In conclusion, USMs can be preferred alternative because accurate targeting of pathologies can occur in free distorted images.

Robust damping control of mobile manipulators.

A novel robust control technique, robust damping control (RDC), is introduced. An RDC controller is further developed for the motion control of a mobile manipulator subject to kinematic constraints. The knowledge of dynamic parameters of the mobile manipulator is assumed to be completely unknown. The proposed RDC controller is capable of disturbance-rejection in the presence of unknown bounded disturbance, without requiring the knowledge of its bound. The stability of the closed-loop system is guaranteed. The controller has a simple structure and can be easily implemented in applications. Experimental tests on a 2-DOF robotic manipulator illustrate that the proposed control is significantly better than conventional robust control.

Neural-network control of mobile manipulators.

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.

Meat quality variation in the robotic sorting of pork loins.

A robot was used to make fiber-optic reflectance measurements from 400 to 700 nm in 10-nm increments at six sites, 10 cm apart, along the length of 48 pork loins. Meat quality was assessed in the longissimus dorsi near the thoracolumbar junction using 1) a bag-drip method for fluid loss, 2) a subjective evaluation of wetness, 3) a colorimeter measurement of paleness (CIE L), and 4) a subjective evaluation for Japanese pork color scores (JPCS). Sorting of the loins in the commercial plant from which they originated was correlated (P < .01) with fluid loss (r=.57), with wetness scores (r=-.57), with CIE L* (r=.71), and with JPCS (r=-.64). Laboratory measurements of pH at the site of meat quality assessment were correlated (P < .01) with fluid loss (r=-.61), with wetness scores (r=.65), with CIE L* (r=-.74), and with JPCS (r=.77). Average spectra obtained robotically were correlated (P < .01) with fluid loss (r=.56 at 670 nm, and R=.76 adding 560 and 540 nm), with wetness score (r=-.65 at 480 nm, and R=.75 adding 530 and 570 nm), with CIE L* (r=.76 at 480 nm, and R=.82 adding 690 and 520 nm), and with JPCS (r=-.70). In sorting loins that were all categorized as normal at the plant, mean reflectance data collected robotically were correlated with fluid loss, r=.42 (P > .05) at 700 nm and R=.58 (P > .05) adding 430 nm; with wetness score, r=.25 (P > .05); with CIE L*, r=.58 (P < .025) at 700 nm; and with JPCS, r=-.71 (P < .01) at 700 nm. Thus, as well as detecting obvious PSE loins, the robotic probe also had a limited capability to sort loins all categorized as normal at the plant.

A review of probes and robots: implementing new technologies in meat evaluation.

Changes in function from fat-depth measurement to meat quality measurement, and vice versa, have occurred in the history of electrical and optical meat probes. In the future, robotic systems might use ultrasonics to measure subcutaneous fat depth while at the same time positioning a fiber-optic probe relative to the skeleton to measure meat quality. There is a major distinction between probes that make a measurement at a single site within the carcass and those that produce a vector of measurements as they move through the carcass. Vector measurements were introduced to find subcutaneous fat thickness, but they may be used for meat quality measurements to deal with intra- and intermuscular variation. Replacement of hand-held probes by robots is in progress and could change meat distribution and marketing, perhaps replacing conventional meat grading by lowering the unit cost of grading and improving reliability for consumers. The feasibility of using ultrasonics to find probe measuring sites in the thoraco-lumbar region of pork carcasses has been proven. This requires new types of carcass morphometry data, such as rib angles and curvatures, and intercostal dimensions.