Research and Publications - Department of Mechanical Engineering

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3D Numerical Modelling of a Rarefied Gas Flow in the Nearby Atmosphere around a Rotating Cometary Nucleus
(2007) Volkov, Alexey N.; Lukyanov, German A.; University of Alabama Tuscaloosa
A combined 3D model of a nearby atmosphere around an arbitrary rotating spherical cometary nucleus is developed. The model includes a 3D unsteady model of solar radiation absorption and heating of the nucleus material (water ice), its evaporation and condensation and a 3D quasi-stationary kinetic model of flow inside the near nucleus coma. This model can be used to predict the coma flow in conditions typical for rendezvous projects such as ESA project Rossetta. Calculations are carried out with the help of this model to reveal the influence of nucleus rotation on its temperature field and the flow field in the nearby atmosphere. It was found that the nucleus rotation influences significantly the nucleus temperature field and the coma flow. Vapor flow around a rotating nucleus is essentially three dimensional and differs qualitatively from the coma flow around a non-rotating nucleus.
A Clustering-based Method for Estimating Pennation Angle from B-mode Ultrasound Images
(Cambridge University Press, 2023-03-01) Bao, Xuefeng; Zhang, Qiang; Fragnito, Natalie; Wang, Jian; Sharma, Nitin
B-mode ultrasound (US) is often used to non-invasively measure skeletal muscle architecture, which contains human intent information. Extracted features from B-mode images can help improve closed-loop human-robotic interaction control when using rehabilitation/assistive devices. The traditional manual approach to inferring the muscle structural features from US images is laborious, time-consuming, and subjective among different investigators. This paper proposes a clustering-based detection method that can mimic a well-trained human expert in identifying fascicle and aponeurosis and, therefore, compute the pennation angle (PA). The clustering-based architecture assumes muscle fibers have tubular characteristics. It is robust for low-frequency image streams. We compared the proposed algorithm to two mature benchmark techniques: UltraTrack and ImageJ. The performance of the proposed approach showed higher accuracy in our dataset (frame frequency is 20Hz), i.e., similar to the human expert. The proposed method shows promising potential in automatic muscle fascicle orientation detection to facilitate implementations in biomechanics modeling, rehabilitation robot control design, and neuromuscular disease diagnosis with low-frequency data stream.
A Deep Learning Method to Predict Ankle Joint Moment During Walking at Different Speeds with Ultrasound Imaging: A Framework for Assistive Devices Control
(Cambridge University Press, 2022) Zhang, Qiang; Fragnito, Natalie; Bao, Xuefeng; Sharma, Nitin
Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance.We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging þ deep learning approach for net joint moment prediction.With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% (p < .001) and increased the prediction coefficient of determination by 20.13% (p < .001). The findings show that the US imaging þ deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assistas- needed control strategy.
A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
(IEEE, 2021) Zhang, Qiang; Iyer, Ashwin; Sun, Ziyue; Kim, Kang; Sharma, Nitin
For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexionmovement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.
A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement
(IEEE, 2023) Zhang, Qiang; Tu, Xikai; Si, Jennie; Lewek, Michael D.; Huang, He
Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.
A Study of Flexible Energy-Saving Joint for Biped Robots Considering Sagittal Plane Motion
(Springer International Publishing Switzerland, 2015-08) Zhang, Qiang; Teng, Lin; Wang, Yang; Xie, Tao; Xiao, Xiaohui
A flexible ankle joint for biped walking robots is proposed to investigate the influence of joint stiffness on motor’s peak torque and energy consumption of the sagittal plane motion during the single support phase. Firstly, an improved model of the inverted pendulum is established, which is the theoretical foundation of the flexible ankle joint. Then the analysis of the analytic method of flexible joint is presented based on the improved model of the inverted pendulum. Finally, dynamic simulations of the flexible joint are performed to examine the correctness of analytic method. The results show that the flexible joint can reduce the joint motor’s peak torque and energy consumption. Furthermore, there is an optimal joint stiffness of the flexible system, which can minimum peak torque with reduction of 45.99% and energy consumption with reduction of 51.65%.
A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist
(IEEE, 2021-05) Sun, Ziyue; Bao, Xuefeng; Zhang, Qiang; Lambeth, Krysten; Sharma, Nitin
During functional electrical stimulation (FES), muscle force saturation and a user’s tolerance levels of stimulation intensity limit a controller’s ability to deliver the desired amount of stimulation, which, if unaddressed, degrade the performance of high-gain feedback control strategies. Additionally, these strategies may overstimulate the muscles, which further contribute to the rapid onset of muscle fatigue. Cooperative control of FES with an electric motor assist may allow stimulation levels within the imposed limits, reduce overall stimulation duty cycle, and compensate for the muscle fatigue. Model predictive controller (MPC) is one such optimal control strategy to achieve these control objectives of the combined hybrid system. However, the traditional MPC method for the hybrid system requires exact model knowledge of the dynamic system, i.e., cannot handle modeling uncertainties, and the recursive feasibility has been shown only for limb regulation problems. So far, extending the current results to a limb tracking problem has been challenging. In this paper, a novel tube-based MPC method for tracking control of a human limb angle by cooperatively using FES and electric motor inputs is derived. A feedback controller for the electrical motor assist is designed such that it reduces the error between the nominal MPC and the output of the actual hybrid system. Further, a terminal controller and terminal constraint region are derived to show the recursive feasibility of the robust MPC scheme. Simulation results were performed on a single degree of freedom knee extension model. The results show robust performance despite modeling uncertainties.
Age-related differences in gait adaptations during overground walking with and without visual perturbations using a virtual reality headset
(Nature Portfolio, 2020) Osaba, Muyinat Y.; Martelli, Dario; Prado, Antonio; Agrawal, Sunil K.; Lalwani, Anil K.; Columbia University; University of Alabama Tuscaloosa; NewYork-Presbyterian Hospital
Older adults have difficulty adapting to new visual information, posing a challenge to maintain balance during walking. Virtual reality can be used to study gait adaptability in response to discordant sensorimotor stimulations. This study aimed to investigate age-related modifications and propensity for visuomotor adaptations due to continuous visual perturbations during overground walking in a virtual reality headset. Twenty old and twelve young subjects walked on an instrumented walkway in real and virtual environments while reacting to antero-posterior and medio-lateral oscillations of the visual field. Mean and variability of spatiotemporal gait parameters were calculated during the first and fifth minutes of walking. A 3-way mixed-design ANOVA was performed to determine the main and interaction effects of group, condition and time. Both groups modified gait similarly, but older adults walked with shorter and slower strides and did not reduce stride velocity or increase stride width variability during medio-lateral perturbations. This may be related to a more conservative and anticipatory strategy as well as a reduced perception of the optic flow. Over time, participants adapted similarly to the perturbations but only younger participants reduced their stride velocity variability. Results provide novel evidence of age- and context-dependent visuomotor adaptations in response to visual perturbations during overground walking and may help to establish new methods for early identification and remediation of gait deficits.
An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton
(IEEE, 2022) Molazadeh, Vahidreza; Zhang, Qiang; Bao, Xuefeng; Sharma, Nitin
A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions through FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this article, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.
An Online Actor-Critic Identifier with Sampled Fatigue Measurements for Optimal Adaptive Control of FES and an Electric motor
(IEEE, 2022) Iyer, Ashwin; Singh, Mayank; Zhang, Qiang; Sun, Ziyue; Sharma, Nitin
Cooperative control of functional electrical stimulation (FES) and electric motors in a hybrid exoskeleton may benefit from fatigue measurements and online model learning. Recent model-based cooperative control approaches rely on time-consuming offline system identification of a complex musculoskeletal system. Further, they may lack the ability to include measurements from muscle sensors that monitor the FESinduced muscle fatigue, which may hinder maintaining desired muscle fatigue levels. This paper develops an online adaptive reinforcement learning approach to control knee extension via an electric motor and FES. An optimal tracking control problem that uses an actor-critic identifier structure is formulated to approximate an optimal solution to the Hamiltonian-Jacobi- Bellman equation. The continuous controller provides asymmetrically saturated optimal control inputs of FES and the electric motor. Critic and identifier neural networks are designed to simultaneously estimate the reward function and the system dynamics based on sampled fatigue measurements and compute control actions. Importantly, simulation results show that a satisfactory joint angle tracking and actuator allocation can be obtained at multiple on-demand desired muscle fatigue levels and prolong FES utilization.
Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study
(IEEE, 2020) Iyer, Ashwin; Sun, Zhiyu; Zhang, Qiang; Kim, Kang; Sharma, Nitin
This paper investigates the use of ultrasound imaging to characterize tremor during a grasping motion. Ultrasound images were collected from three human participants including an able-bodied participant, a patient with Parkinson’s disease, and a patient with essential tremor. Each human participant was instructed to grasp and hold objects with three different masses in a vertical upright position with an ultrasound probe strapped to their forearm while seated. The images were processed using an ultrasound speckle tracking algorithm to measure muscle strain during the grasping and holding motion. Analysis of the computed strain values showed marked differences in the strain peaks and frequencies between able-bodied participant and the patients with tremor. The detected frequencies depict how the strain measurement changes during the grasping and holding motion. The frequency for tremor participants fall within accepted frequency ranges for Parkinson’s Disease and Essential Tremor, and thus can be representative of the actual tremor frequency.
Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography
(IEEE Explore, 2019) Zhang, Qiang; Sheng, Zhiyu; Moore-Clingenpeel, Frank; Kim, Kang; Sharma, Nitin
Ankle dorsiflexion produced by Tibialis Anterior (TA) muscle contraction plays a significant role during human walking and standing balance. The weakened function or dysfunction of the TA muscle often impedes activities of daily living (ADL). Powered ankle exoskeleton is a prevalent technique to treat this pathology, and its intelligent and effective behaviors depend on human intention detection. A TA muscle contraction strength monitor is proposed to evaluate the weakness of the ankle dorsiflexion. The new method combines surface electromyography (sEMG) signals and sonomyography signals to estimate ankle torque during a voluntary isometric ankle dorsiflexion. Changes in the pennation angle (PA) are derived from the sonomyography signals. The results demonstrate strong correlations among the sonomyography-derived PA, the sEMG signal, and the measured TA muscle contraction force. Especially, the TA muscle strength monitor approximates the TA muscle strength measurement via a weighted summation of the sEMG signal and the PA signal. The new method shows an improved linear correlation with the muscle strength, compared to the correlations between the muscle strength and sole sEMG signal or sole PA signal, where the R-squared values are improved by 4.21 % and 1.99 %, respectively.
AnkleImage - An ultrafast ultrasound image dataset to understand the ankle joint muscle contractility (See Full Item Page for link to data)
(2024) Zhang, Qiang; Akinniyi, Oluwasegun
The role of the human ankle joint in activities of daily living, including walking, maintaining balance, and participating in sports, is of paramount importance. Ankle joint dorsiflexion and plantarflexion functionalities mainly account for ground clearance and propulsion power generation during locomotion tasks, where those functionalities are driven by the contraction of ankle joint skeleton muscles. Studies of corresponding muscle contractility during ankle dynamic functions will facilitate us to better understand the joint torque/power generation mechanism, better diagnose potential muscular disorders on the ankle joint, or better develop wearable assistive/rehabilitative robotic devices that assist in community ambulation. This data descriptor reports a new dataset that includes the ankle joint kinematics/kinetics, associated muscle surface electromyography, and ultrafast ultrasound images with various annotations, such as pennation angle, fascicle length, tissue displacements, echogenicity, and muscle thickness, of ten healthy participants when performing volitional isometric, isokinetic, and dynamic ankle joint functions (walking at multiple treadmill speeds, including 0.50 m/s, 0.75 m/s, 1.00 m/s, 1.25 m/s, and 1.50 m/s). Data were recorded by a research-use ultrasound machine, a self-designed ankle testbed, an inertia measurement unit system, a Vicon motion capture system, a surface electromyography system, and an instrumented treadmill. The descriptor in this work presents the results of a data curation or collection exercise from previous works, rather than describing a novel primary/experimental data collection.
Assessing the validity, reliability, and practicality of ASHRAE's performance measurement protocols (ASHRAE Research Project 1702)
(Taylor & Francis, 2019) Wang, Liping; Mcmorrow, Gabrielle; Zhou, Xiaohui; O'Neill, Zheng D.; University of Wyoming; University of Alabama Tuscaloosa
The objective of this study was to provide a basis for future updates to the 2010 ASHRAE Performance Measurement Protocols for Commercial Buildings (PMP) through case studies. The PMP defines a standardized method of measuring and analyzing building performance in six categories: energy, water, thermal comfort, indoor air quality, lighting, and acoustics. We conducted case studies for five buildings following the PMP. Based on experiences following the protocol in this wide range of buildings, we assessed the validity, reliability, and practicality of the PMP and provided comments and recommendations for future revisions. Most of the measurement protocols at the basic level are reliable, practical, and valid. Many tasks at the intermediate and advanced levels, however, can be difficult to perform for some building types. Some of the tasks or measurement procedures were results from past research projects, and the software or tools recommended may not be readily available or fully supported. The measurement protocols at the intermediate level are only somewhat reliable and some of them are impractical. Most measurement protocols at the advanced level are complex and need to be performed by qualified or specially trained personnel and, thus, are impractical as a performance measure, except for specialized applications.
Atomistic simulations, mesoscopic modeling, and theoretical analysis of thermal conductivity of bundles composed of carbon nanotubes
(American Institute of Physics, 2013-09-10) Volkov, Alexey N.; Salaway, Richard N.; Zhigilei, Leonid V.; University of Virginia; University of Alabama Tuscaloosa
The propensity of carbon nanotubes (CNTs) to self-organize into continuous networks of bundles has direct implications for thermal transport properties of CNT network materials and defines the importance of clear understanding of the mechanisms and scaling laws governing the heat transfer within the primary building blocks of the network structures-close-packed bundles of CNTs. A comprehensive study of the thermal conductivity of CNT bundles is performed with a combination of non-equilibrium molecular dynamics (MD) simulations of heat transfer between adjacent CNTs and the intrinsic conductivity of CNTs in a bundle with a theoretical analysis that reveals the connections between the structure and thermal transport properties of CNT bundles. The results of MD simulations of heat transfer in CNT bundles consisting of up to 7 CNTs suggest that, contrary to the widespread notion of strongly reduced conductivity of CNTs in bundles, van der Waals interactions between defect-free well-aligned CNTs in a bundle have negligible effect on the intrinsic conductivity of the CNTs. The simulations of inter-tube heat conduction performed for partially overlapping parallel CNTs indicate that the conductance through the overlap region is proportional to the length of the overlap for CNTs and CNT-CNT overlaps longer than several tens of nm. Based on the predictions of the MD simulations, a mesoscopic-level model is developed and applied for theoretical analysis and numerical modeling of heat transfer in bundles consisting of CNTs with infinitely large and finite intrinsic thermal conductivities. The general scaling laws predicting the quadratic dependence of the bundle conductivity on the length of individual CNTs in the case when the thermal transport is controlled by the inter-tube conductance and the independence of the CNT length in another limiting case when the intrinsic conductivity of CNTs plays the dominant role are derived. An application of the scaling laws to bundles of single-walled (10,10) CNTs reveals that the transition from inter-tube-conductance-dominated to intrinsic-conductivity-dominated thermal transport in CNT bundles occurs in a practically important range of CNT length from similar to 20 nm to similar to 4 mu m. (C) 2013 AIP Publishing LLC.
Characterization Of Multi-layered Fish Scales (Atractosteus spatula) Using Nanoindentation, X-ray CT, FTIR, and SEM
(MyJove Corporation, 2014) Allison, Paul G.; Rodriguez, Rogie I.; Moser, Robert D.; Williams, Brett A.; Poda, Aimee R.; Seiter, Jennifer M.; Lafferty, Brandon J.; Kennedy, Alan J.; Chandler, Mei Q.; United States Department of Defense; United States Army; U.S. Army Corps of Engineers; U.S. Army Engineer Research & Development Center (ERDC); University of Alabama Tuscaloosa
The hierarchical architecture of protective biological materials such as mineralized fish scales, gastropod shells, ram's horn, antlers, and turtle shells provides unique design principles with potentials for guiding the design of protective materials and systems in the future. Understanding the structure-property relationships for these material systems at the microscale and nanoscale where failure initiates is essential. Currently, experimental techniques such as nanoindentation, X-ray CT, and SEM provide researchers with a way to correlate the mechanical behavior with hierarchical microstructures of these material systems. However, a well-defined standard procedure for specimen preparation of mineralized biomaterials is not currently available. In this study, the methods for probing spatially correlated chemical, structural, and mechanical properties of the multilayered scale of A. spatula using nanoindentation, FTIR, SEM, with energy-dispersive X-ray (EDX) microanalysis, and X-ray CT are presented.
Closing the Loop Between Wearable Robots and Machine Learning: A New Paradigm for Steering Assistance Personalization Control
(Springer Nature, 2024-07-24) Zhang, Qiang; Zanotte, Damiano; Sharifi, Motjaba; Kim, Myunghee; Li, Zhijun
Lower-extremity wearable robotic devices, first introduced in the early 2000s, have been developed to enhance human mobility and support therapeutic training for patients. Recent advancements in human-in-the-loop (HIL) optimization have significantly improved the control of these devices, fine-tuning the interaction between humans and robots. This has led to more personalized assistance for daily living activities and rehabilitation training. Our comprehensive and extensive literature review, spanning from January 2017 to December 2023, highlights 34 noteworthy studies that have demonstrated enhanced human locomotion performance through HIL-optimized and personalized assistance. This review explores pivotal innovations and methodologies for controlling lower-extremity robotic exoskeletons, exosuits, and prostheses. It covers the establishment of control objectives, the application of various optimization methods, and the assessment of outcomes. Additionally, we provide a comparative analysis of the HIL optimization method against alternative control strategies, such as those based on reinforcement learning. Looking forward, we discuss expected trends that aim to enhance the efficacy of wearable robotic devices. We also recognize the challenges that need to be addressed to fully realize the benefits of customized gait assistance for individuals with lower-extremity impairments or neurological conditions. This includes technological, regulatory, and user-centered issues that could impact the widespread adoption and effectiveness of these innovative systems.
Compact Shape Morphing Tensegrity Robots Capable of Locomotion
(Frontiers, 2019) Rhodes, Tyler; Gotberg, Clayton; Vikas, Vishesh; University of Alabama Tuscaloosa
Robustness, compactness, and portability of tensegrity robots make them suitable candidates for locomotion on unknown terrains. Despite these advantages, challenges remain relating to ease of fabrication, shape morphing (packing-unpacking), and locomotion capabilities. The paper introduces a design methodology for fabricating tensegrity robots of varying morphologies with modular components. The design methodology utilizes perforated links, coplanar (2D) alignment of components and individual cable tensioning to achieve a 3D tensegrity structure. These techniques are utilized to fabricate prism (three-link) tensegrity structures, followed by tensegrity robots in icosahedron (six-link), and shpericon (curved two-link) formation. The methodology is used to explore different robot morphologies that attempt to minimize structural complexity (number of elements) while facilitating smooth locomotion (impact between robot and surface). Locomotion strategies for such robots involve altering the position of center-of-mass (referred to as internal mass shifting) to induce "tip-over." As an example, a sphericon formation comprising of two orthogonally placed circular arcs with conincident center illustrates smooth locomotion along a line (one degree of freedom). The design of curved links of tensegrity mechanisms facilitates continuous change of the point of contact (along the curve) that results from the tip-over. This contrasts to the sudden and piece-wise continuous change for the case of robots with traditional straight links which generate impulse reaction forces during locomotion. The two resulting robots-the Icosahedron and the Sphericon Tensegrity Robots-display shape morphing (packing-unpacking) capabilities and achieve locomotion through internal mass-shifting. The presented static equilibrium analysis of sphericon with mass is the first step in the direction of dynamic locomotion control of these curved link robots.
Compliant Joint for Bipod Robot Considering Energy Consumption Optimization
(Springer, 2015-11) Zhang, Qiang; Xiao, Xiaohui; Wang, Yang; You, Penghui; Xie, Tao
Abstract: In order to optimize the energy consumption of biped robot while walking, the compliant joint for biped walking robots is proposed to investigate the influence of ankle joint and knee joint stiffness on motor torque and energy consumption of the sagittal plane motion during the single support phase. Firstly, an improved model of the five-link biped robot is established, which is the theoretical foundation of the compliant joint. Then, with the method of gait planning based on natural Zero Moment Point (ZMP) trajectory, the robot’s center of mass (COM) track is obtained by setting reference of ZMP trajectory and the gait on a rigid path is acquired by interpolation. Finally, both the Lagrange equations analytic method and dynamic simulations are performed to analyze the influences of compliant joint stiffness on motor torque and energy consumption based on the improved model of the five-link biped robot. The results show that the compliant joint can reduce the joint motor torque and energy consumption effectively. Furthermore, there is an optimal stiffness of the compliant ankle joint and knee joint respectively, which can minimum the motor energy consumption with reduction of 89.87% and 90.11% in analytic method, as well as 88.66% and 81.23% in dynamic simulations.
A CRITERION FOR THE VALIDITY OF PARKER'S MODEL IN THERMAL ESCAPE PROBLEMS FOR PLANETARY ATMOSPHERES
(IOP Publishing, 2015-10-01) Volkov, A. N.; University of Alabama Tuscaloosa
Mass escape rate of mon- and diatomic gases from a planetary atmosphere is studied based on Parker's model for a broad range of surface conditions. The escape rate is found to follow two asymptotic regimes, namely, high-and low-density regimes, with a short intermediate regime between them. Equations for the escape rate in every asymptotic regime are found theoretically. A comparison of the obtained escape rates with results of recent kinetic simulations shows that Parker's model satisfactorily predicts escape rates only in the high-density regime. Based on this finding, a criterion of applicability of Parker's model for the calculation of the mass escape rate is established.

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