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Collaborative robots are more and more used in smart manufacturing because of their capability to work beside and collaborate with human workers. With the deployment of these robots, manufacturing tasks are more inclined to be accomplished by multiple humans and multiple robots (MH-MR) through teaming effort. In such MH-MR collaboration scenarios, the task distribution among the multiple humans and multiple robots is very critical to efficiency. It is also more challenging due to the heterogeneity of different agents. Existing approaches in task distribution among multiple agents mostly consider humans with assumed or known capabilities. However human capabilities are always changing due to various factors, which may lead to suboptimal efficiency. Although some researches have studied several human factors in manufacturing and applied them to adjust the robot task and behaviors.

The current work addresses efforts to characterize multiple-evaporator capillary pumped loops. Both experimental and analytical approaches were used to predict performance of parallel evaporators and corresponding effects from adjacent operating evaporators. The effects of low and high power dissipation and the distribution of powers among the evaporators were tested. Additionally, a pressure balance model is given where the maximum heat transfer capacity for an evaporator operating under a multi-evaporator condition is determined based on pressure distribution throughout the loop. The model and experiment comparisons demonstrated how the heat load distribution among evaporators affects the maximum capillary limit for individual evaporators operating in a multiple evaporator mode.

The Deep Orange program immerses automotive engineering students into the world of an OEM as part of their 2-year graduate education. In support of developing the program’s seventh vehicle concept, the students studied the sponsoring brand essence, conducted market research, and made a heuristic assessment of competitor vehicles. The upfront research lead to the definition of target customers and setting vehicle level targets that were broken down into requirements to develop various vehicle sub-systems. The powertrain team was challenged to develop a scalable propulsion concept enabled by a common vehicle architecture that allowed future customers to select (at the point of purchase) among various levels of electrification best suiting their needs and personal desires. Four different configurations were identified and developed: all-electric, two plug-in hybrid electric configurations, and an internal combustion engine only.

Determining the validity of the design space early in the conceptualization of a project can make the difference between project success and failure. Early assessment of technical feasibility, project risk, technical readiness and realistic performance expectations based on models with different levels of fidelity, uncertainty, and technical robustness is a challenging mission critical task for large procurement projects. Tradespace exploration uses model-based engineering analysis, design exploration methods, and multi-objective optimization techniques to enable project stakeholders to make informed decisions and tradeoffs concerning the scope, schedule, budget, performance and risk profile of a project. As the intersection with a number of project stakeholders, tradespace studies can provide a significant impact upon the direction and decision-making in a project.

Improper fit in a vehicle will affect a driver’s ability to reach the steering wheel and pedals, view the roadway and instrument gauges, and allow vehicle safety features to protect the driver during a crash. CarFit® is a community outreach program to educate older drivers on proper “fit” within their personal vehicle. A subset of measurements from CarFit® were used to quantify the “fit” of 97 older drivers over 60 and 20 younger drivers, ages 30-39, in their personal vehicles. Binary, logistic regression was used to assess the likelihood of drivers meeting the CarFit® measurement criteria prior to and after CarFit® education. The results showed older drivers were five times more likely than younger drivers to meet the CarFit® criteria for line of sight above the steering wheel, suggesting that younger drivers would also benefit from CarFit® education.

Traditional Electronic Stability Control (ESC) for automobiles is usually accomplished through the use of estimated vehicle dynamics from simplified models that rely on parameters such as cornering stiffness that can change with the vehicle state and time. This paper proposes a different method for electronic stability control of oversteer by predicting the degree of instability in a vehicle. The algorithm is solely based on measurable response characteristics including lateral acceleration, yaw rate, speed, and driver steering input. These signals are appropriately conditioned and evaluated with fuzzy logic to determine the degree of instability present. When the “degree of instability” passes a certain threshold, the appropriate control action is applied to the vehicle in the form of differential yaw braking. Using only the measured response of the vehicle alleviates the problem of degraded performance when vehicle parameters change.

This paper proposes and compares torque distribution management strategies for vehicle stability control (VSC) of vehicles with independently driven wheels. For each strategy, the following feedback control variables are considered turn by turn: 1) yaw rate 2) lateral acceleration 3) both yaw rate and lateral acceleration. Computer simulation studies are conducted on the effects of road friction conditions, feedback controller gains, and a driver emulating speed controller. The simulation results indicated that all VSC torque management strategies are generally very effective in tracking the reference yaw rate and lateral acceleration of the vehicle on both dry and slippery surface conditions. Under the VSC strategies employed and the test conditions considered, the sideslip angle of the vehicle remained very small and always below the desired or target values.

Autonomous driving technologies can provide better safety, comfort and efficiency for future transportation systems. Most research in this area has mainly been focused on developing sensing and control approaches to achieve various autonomous driving functions. Very little of this research, however, has studied how to efficiently handle sensing exceptions. A simple exception measured by any of the sensors may lead to failures in autonomous driving functions. The autonomous vehicles are then supposed to be sent back to manufacturers for repair, which takes both time and money. This paper introduces an efficient approach to make human drivers able to online teach autonomous vehicles to drive under sensing exceptions. A human-vehicle teaching-and-learning framework for autonomous driving is proposed and the human teaching and vehicle learning processes for handling sensing exceptions in autonomous vehicles are designed in detail.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.