Steering of continuous fiber along three-dimensional (3D) paths in automated fiber placement (AFP) additive manufacturing using a 6-axis robotic arm requires advanced toolpath planning strategies to ensure coordinated control of robotic movements, printing speed, and deposition temperature. Fiber steering requires large nozzle rotations to keep the fibers tangential to the nozzle path. If the print speed is not reduced accordingly, the resulting large robot joint accelerations cause jerky movements and vibrations that disrupt the precise printing height—typically ranging from 0.1 to 0.3 mm—causing fiber damage at the nozzle tip and path errors. This research introduces a novel approach called Maneuverability-based Speed and Temperature Adaptive Robotic Control (M-STARC). The method dynamically adjusts printing speed and deposition temperature based on the complexity of the robotic joints' maneuvering required to maintain tangential alignment of the 3D printing nozzle with the fiber path trajectory. Heat transfer analyses determine nozzle temperature as a function of printing speed. This speed is varied along the trajectory to limit robot joint accelerations, which depend on the maneuverability (kinematics) of the robot. Faster printing speeds (and higher nozzle temperatures) are allowed at points where less maneuvering is needed. The proposed toolpath planning approach effectively defines the 3D path and robotic movements while adhering to critical speed–temperature constraints, laying the theoretical foundation for future experimental validation and implementation in fiber steering applications.

In this paper, we propose an approach to facilitate the identification of threshold concepts in undergraduate engineering curricula. The approach is based on the framework of transactional curriculum inquiry where educators work with a group of stakeholders (students, curriculum designers, industry practitioners) to identify threshold concepts. Our proposed approach involves developing a participatory simulation using agent-based modeling that will serve as a digital forum for the exploration of threshold concepts in engineering courses.

Low amplitude mechanical noise vibration has been shown to improve somatosensory acuity in various clinical groups with comparable deficiencies through a phenomenon known as Stochastic Resonance (SR). This technology showed promising outcomes in improving somatosensory acuity in other clinical patients (e.g., Parkinson's disease and osteoarthritis). Some degree of chronic somatosensory deficiency in the knee has been reported following anterior cruciate ligament (ACL) reconstruction surgery. In this study, the effect of the SR phenomenon on improving knee somatosensory acuity (proprioception and kinesthesia) in female ACL reconstructed (ACLR) participants (n =19) was tested at three months post surgery, and the results were compared to healthy controls (n = 28). Proprioception was quantified by the measure of joint position sense (JPS) and kinesthesia with the threshold to detection of passive movement (TDPM). The results based on the statistical analysis demonstrated an overall difference between the somatosensory acuity in the ACLR limb compared to healthy controls (p = 0.007). A larger TDPM was observed in the ACLR limb compared to the healthy controls (p = 0.002). However, the JPS between the ACLR and healthy limbs were not statistically significantly different (p = 0.365). SR significantly improved JPS (p = 0.006) while the effect was more pronounced in the ACLR cohort. The effect on the TDPM did not reach statistical significance (p = 0.681) in either group. In conclusion, deficient kinesthesia in the ACLR limb was observed at three months post-surgery. Also, the positive effects of SR on somatosensory acuity in the ACL reconstructed group warrant further investigation into the use of this phenomenon to improve proprioception in ACLR and healthy groups. (C) 2018 Elsevier Ltd. All rights reserved.

Accurately estimating hand and finger poses for recognizing gestures helps solve many technical challenges, such as controlling prosthetics, robotic manipulation, and computer input, e.g. for virtual reality. A major challenge is accurately identifying gestures under different conditions-such as mobile or low-light environments-without hindering hand function. This paper describes a low-cost wrist-mounted device that uses piezoelectric sensors to estimate finger gestures. The signals that are recorded are vibrations and shape changes that occur at the wrist due to muscle and tendon motion. An array of six piezoelectric sensors was affixed to the inside of an adjustable wrist strap. A user study was completed. To identify when a subject made a finger tap gesture, a touch graphics tablet recorded when a fingertip contacted the tablet surface. Piezoelectric signal features were computed over timing windows coinciding with a gesture. The features were used in the training of a support vector machine classification model. The results indicate the viability of using piezoelectric sensors to classify finger tap gestures, with a mean classification accuracy of 97% for tap gestures made with each of the five fingers.

We derive an adaptive Lyapunov backstepping scheme to achieve hybrid force-position control of a revolute-joint robotic manipulator. It is suitable for the situation where the desired force and desired trajectory motion are perpendicular i.e. for operating on a flat surface. The control also tracks commands in free space so that no switching is required when encountering/leaving the surface. The control utilizes the robot parameters but neural networks adaptively model the environmental effects. The proof of stability requires an assumption of a passive mapping from velocity to force and that the environment can be modelled as a nonlinear stiffness. Simulation results show the proposed neural-adaptive solution can, without any pre-training, significantly outperform linear methods in both position and force tracking.

In this paper, we demonstrate a novel approach for the use of piezoelectric pressure sensors on a wearable wrist band to detect the occurrence of individual finger gestures. The system is designed to be wearer independent and require no training for the wearer or the system to perform accurate gesture detection. We used continuous signal windowing to identify bulk changes of several signal features before, during, and after a gesture was made by the wearer. For a range of window lengths and two filtering choices, a thresholding method was applied to the signals to determine the occurrence of a gesture. The error rate for missed and incorrectly labelled gestures were calculated for unfiltered and filtered data, as well as for various window lengths and thresholds. We found that longer window lengths resulted in fewer errors, that the maximum value within a window was a good indicator of whether an event occurred, and that thresholds needed to be sufficiently greater than the signal average, but too large a threshold value leads to missed events.

Spinal manipulative therapy (SMT) creates health benefits for some while for others, no benefit or even adverse events. Understanding these differential responses is important to optimize patient care and safety. Toward this, characterizing how loads created by SMT relate to those created by typical motions is fundamental. Using robotic testing, it is now possible to make these comparisons to determine if SMT generates unique loading scenarios. In 12 porcine cadavers, SMT and passive motions were applied to the L3/L4 segment and the resulting kinematics tracked. The L3/L4 segment was removed, mounted in a parallel robot and kinematics of SMT and passive movements replayed robotically. The resulting forces experienced by L3/L4 were collected. Overall, SMT created both significantly greater and smaller loads compared to passive motions, with SMT generating greater anterioposterior peak force (the direction of force application) compared to all passive motions. In some comparisons, SMT did not create significantly different loads in the intact specimen, but did so in specific spinal tissues. Despite methodological differences between studies, SMT forces and loading rates fell below published injury values. Future studies are warranted to understand if loading scenarios unique to SMT confer its differential therapeutic effects.

The average adult spends more hours per day interacting with a computer than sleeping. Computer interfaces that require low physical effort offer users a heathy and efficient interaction method. The lowest physical effort device is the brain-computer interface, which uses electric signals on the scalp. However, since electroencephalography signals are difficult to detect and process, we are investigating the use of alternative biosignals suitable for wearable computer interfaces. Sensors worn over or near muscles can detect electromyographic or mechanomyographic signals, where the latter refer to vibrations and pressure changes caused by muscle activation. Previously, mechanomyographic signals have been measured using accelerometers, microphones and other vibration sensing equipment, and some wearable computer interfaces based on muscle activation have been investigated. We are instead using piezo-electric sensors to measure vibration and pressure, as they are inexpensive, small and highly sensitive. Using piezo-electric sensors, we developed a wrist wearable sensor array that allowed unrestricted movement of the fingers and produced a recordable signal. The movement generated signals were recorded during experiments involving small individual finger movements. Each isolated movement was associated with a single recording which was analysed for a variety of signal features, correlation with the movement, and repeatability between sessions. The correlation and repeatability results support the use of piezo-electric sensors as a viable wearable computer interface sensor. Such a device could be used for prosthetic control, robot assisted surgery, and mobile computer interaction.

We describe how the algebra of relations provides a suitable framework for the study of interconnected dynamic systems and enriches students' understanding of systems, circuits, machines, processes, and feedback control.Compared to the traditional approach based on transfer functions, the theory is shown to be simpler yet more general and rigorous.Previously introduced in the technical literature within the setting of abstract algebra, the theory is presented here in a less general but more accessible manner.We also introduce some new concepts and constructs that increase its utility and pedagogical value.These include relation diagrams (the counterpart of traditional block diagrams) and impedance relations.Examples illustrate applications of the theory and its potential benefits for engineering education.

This paper reports on the dynamic analysis and experimental validation of a method to perturb the balance of subjects in quiet standing. Electronically released weights pull the subject's waist through a specified displacement sensed by a photoelectric sensor. A dynamic model is derived that computes the force applied to the subject as a function of waist acceleration. This model accurately predicts the acceleration of mock subjects (suspended masses) with high repeatability. The validity and simplicity of this model suggest that this method can provide a standard for provocation testing on stable surfaces. Proof-of-concept trials on human subjects demonstrate that the device can be used with a force platform and motion tracking and that the device can induce both sway and step recoveries in healthy male adults.

An adaptive control for visual servoing is presented that is robust to parametric uncertainties in the robot arm model and in the camera calibration and feature tracking system. The control includes a tuning parameter for balancing nominal performance and stability robustness. The control is implemented on a PUMA robot driven by an open-architecture controller. Experiments validate the theoretical results and compare the performance of the proposed control to that of PID control and quasi-Newton adaptive control.

If M is an R-module over an abelian ring R, then the set of all total submodules of M2 is a seminearring (T, + , ·), where (+) is relation addition, and (·) is composition. If B is a Bezout domain of linear surjections on M, we construct a subseminearring Q of T consisting of so-called rational relations on M. An example is the set Q of single-input single-output relations defined by linear time-invariant (LTI) differential equations. A subseminearring of this Q is the field F of transfer functions, which approximate such relations as operators by neglecting their free response. Since rational relations include the free response, we propose using them instead of transfer functions to model and analyze LTI systems. Connections to results in behavioral systems theory are described.

This paper outlines the control algorithms used in a pan / tilt / zoom (PTZ) tracking system for an Unmanned Ground Vehicle (UGV), implemented as part of a computer-vision based autonomous convoying system. The system relies upon a Linear Quadratic Gaussian controller to keep the target centered in the camera's field of view using the pan and tilt degrees of freedom. A novel zoom controller, based upon the current target size and various measures of system noise, controls the camera focal length to maintain an appropriate image size for visual tracking, despite changing distances between the leader and follower vehicles.

We consider the control of Port-Controlled Hamiltonian (PCH) systems, which are a generalization of Euler-Lagrange Systems. A new matching equation for PCH systems is developed so that Interconnection Damping Assignment Passivity-Based Control (IDA-PBC) can be extended to the regulation of some underactuated PCH systems whose kinetic energy must be modified. A simple underactuated mechanical system (the inertial wheel pendulum) is used to demonstrate the effectiveness of the proposed method.

To control Euler-Lagrange (EL) systems, we propose an adaptive control which is itself EL. Through a novel interconnection of these two passive systems, the closed-loop kinetic energy is modified by the potential energy of the EL controller, while a switched update law is used to update the dynamic model. It is proved that the control guarantees convergence to zero tracking error, while simulations show that it achieves faster convergence and better transient performance than an established adaptive control.

The authors extend energy-balancing-based control for set-point regulation to the problem of trajectory tracking for Euler-Lagrange (EL) systems. In addition to producing a new tracking control, this method also provides a re-interpretation of established results, such as computed torque control, PD+ control and Slotine-Li control. The authors also consider port-controlled Hamiltonian (PCH) systems, which are more general than EL systems. They develop a new matching equation for PCH systems so that interconnection damping assignment passivity-based control (IDA-PBC) can be applied to the control of a larger class of under-actuated PCH systems and the tracking control of some non-passive systems.

The collective behaviour of swarms produces smarter actions than those achieved by a single individual. Colonies of ants, flocks of birds and fish schools are examples of swarms interacting with their environment to achieve a common goal. This cooperative biological intelligence is the inspiration for an adaptive fuzzy controller developed in this paper. Swarm intelligence is used to adjust the parameters of the membership functions used in the adaptive fuzzy controller. The rules of the controller are designed using a computing-with-words approach called Fuzzy-Lyapunov synthesis to improve the stability and robustness of an adaptive fuzzy controller. Computing-with-words provides a powerful tool to manipulate numbers and symbols, like words in a natural language.

The problem of robust control is considered for uncertain systems with time-varying input delays. A new Lyapunov-Krasovskii functional is used together with a neutral transformation to design a robust controller for larger bounds on plant uncertainties than permitted by a recently published method. Numerical examples verify the analysis results.

Visual tracking is an important part of artificial Vision for robotics. it allows robots to move towards a desired position using real world information. In this paper we present a novel particle filtering method for visual tracking, based on a clonal selection and a somatic mutation processes used by the natural immune system, which is excellent at identifying intrusion cells; antigens. This capability is used in this work to track motion of the object in a sequence of images.