Browse Topic: Kinematics
Ongoing research in simulated vehicle crash environments utilizes postmortem human subjects (PMHS) as the closest approximation to live human response. Lumbar spine injuries are common in vehicle crashes, necessitating accurate assessment methods of lumbar loads. This study evaluates the effectiveness of lumbar intervertebral disc (IVD) pressure sensors in detecting various loading conditions on component PMHS lumbar spines, aiming to develop a reliable insertion method and assess sensor performance under different loading scenarios. The pressure sensor insertion method development involved selecting a suitable sensor, using a customized needle-insertion technique, and precisely placing sensors into the center of lumbar IVDs. Computed tomography (CT) scans were utilized to determine insertion depth and location, ensuring minimal tissue disruption during sensor insertion. Tests were conducted on PMHS lumbar spines using a robotic test system for controlled loading in flexion
In the 1990s and early 2000s, the field of parallel kinematics was viewed as being potentially transformational in manufacturing, having multiple potential advantages over conventional serial machine tools and robots. Many prototypes were developed, and some reached commercial production and implementation in areas such as hard material machining and particularly in aerospace manufacturing and assembly. There is some activity limited to niche and specialist applications; however, the technology never quite achieved the market penetration and success envisaged. Yet, many of the inherent advantages still exist in terms of stiffness, force capability, and flexibility when compared to more conventional machine structures. This chapter will attempt to identify why parallel kinematic machines (PKMs) have not lived up to the original excitement and market interest and what needs to be done to rekindle that interest. In support of this, a number of key questions and issues have been identified
Innovators at NASA Johnson Space Center have developed a programmable steering wheel called the Tri-Rotor, which allows an astronaut the ability to easily operate a vehicle on the surface of a planet or Moon despite the limited dexterity of their spacesuit. This technology was originally conceived for the operation of a lunar terrain vehicle (LTV) to improve upon previous Apolloera hand controllers. In re-evaluating the kinematics of the spacesuit, such as the rotatable wrist joint and the constant volume shoulder joint, engineers developed an enhanced and programmable hand controller that became the Tri-Rotor
Pyrotechnic seat belt pretensioners typically remove 8–15 cm of belt slack and help couple an occupant to the seat. Our study investigated pretensioner deployment on forward-leaning, live volunteers. The forward-leaning position was chosen because research indicates that passengers frequently depart from a standard sitting position. Characteristics of the 3D kinematics of forward-leaning volunteers following pretensioner deployment determines if body size is correlated with subject response. Nine adult subjects (three female), ages 18–43 years old, across a wide range of body sizes (50–120 kg) were tested. The age was limited to young, active adults as pyrotechnic pretensioners can deliver a notable force to the trunk. Subjects assumed a forward-leaning position, with 26 cm between C7 and the headrest, in a laboratory setting that replicated the passenger seat of a vehicle. At an unexpected time, the pretensioner was deployed. 3D kinematics were measured through a nine-camera motion
This document establishes acceptable design criteria for instrument and cockpit illumination for general aviation aircraft
A powered, single-strained electronic skin sensor was developed that can capture human motion from a distance. The strain sensor, placed on the wrist, decodes complex five-finger motions in real time with a virtual 3D hand that mirrors the original motions. The deep neural network boosted by rapid situation learning (RSL) ensures stable operation regardless of its position on the surface of the skin
Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (vrot) and the corresponding rotational acceleration (arot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) “baseline”, using random initial weights; 2) “transfer learning”, using weight transfer from a previous CNN model trained on
With the development of science and technology, breakthroughs have been made in the fields of intelligent algorithms, environmental perception, chip embedding, scene analysis, and multi-information fusion, which has prompted the wide attention of society, manufacturers and owners of autonomous vehicles. As one of the key issues in the research of autonomous vehicles, the research of vehicle lane change algorithm is of great significance to the safety of vehicle driving. This paper focuses on the conflict of interest between the lane-changing vehicle and the target lane vehicle in the fully autonomous driving environment, and proposes the method of coupling kinematics and game theory, so that when the vehicle is in the process of lane changing game, the lane-changing vehicle and the target lane vehicle can make decisions that are beneficial to the balance of interests of both sides. This paper first proposes the conditions for judging whether the lane-changing vehicle and the target
In recent years, intelligent driving technology is being extensively studied. This paper proposes a path planning method for perpendicular parking based on vehicle kinematics model using MPC optimization, which aims to solve the perpendicular parking task. Firstly, in the case of any initial position and orientation of the vehicle, judging whether the vehicle can be parked at one step according to the location of the parking place and the width of the lane, and then calculating the starting position for parking, and use the Bezier curve to connect the initial position and the starting position. Secondly, reference parking path is calculated according to the collision constraints of the parking space. Finally, because the parking path based on the vehicle kinematics model is composed of circle arcs and straight lines, the curvature of the path is discontinuous. The reference parking path is optimized using Model Predictive Control (MPC). The final path is easier to be followed, and
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