The Spherical Underactuated Planetary Exploration Robot ball (SUPERball) is an ongoing project within NASA Ames Research Center's Intelligent Robotics Group and the Dynamic Tensegrity Robotics Lab (DTRL). The current SUPERball is the first full prototype of this tensegrity robot platform, eventually destined for space exploration missions. This work, building on prior published discussions of individual components, presents the fully-constructed robot. Various design improvements are discussed, as well as testing results of the sensors and actuators that illustrate system performance. Basic low-level motor position controls are implemented and validated against sensor data, which show SUPERball to be uniquely suited for highly dynamic state trajectory tracking. Finally, SUPERball is shown in a simple example of locomotion. This implementation of a basic motion primitive shows SUPERball in untethered control.

Background The prevalence of food insecurity (FI) as “the limited or uncertain availability of enough food for an always active and healthy life” and diabetes as “the most common metabolic disease” are rising in Iran. The aim was to assess the FI, depression, and socioeconomic status as risk factors for type 2 diabetes (T2D). Methods This case-control study was conducted on 135 patients with T2D as cases (99 females, 36 males, mean age 46.83 years) and 135 subjects without diabetes (89 females, 46 males, mean age 45.93 years) as controls. They had been referred to clinics of Shiraz University of Medical Sciences, Shiraz, Iran. The prior major inclusion criterion for diabetes was fasting blood sugar (FBS) ≥126 mg/dl. General, demographic, and socioeconomic characteristics and FI status were assessed using the general and 18-items United States Department of Agriculture (USDA) household food security questionnaires, respectively. Chi-square, t-test, and uni-and multi-variate logistic regression tests and SPSS 16 statistical software were used. Results The prevalence of FI was 66.7% in cases and 41.5% in controls. According to final analysis model, FI (Odds Ratio [OR] = 1.9, P = 0.016), depression (OR = 2.0, P = 0.018), body mass index (BMI) ≥ 25 kg/m 2 (OR = 1.8, P = 0.025), number of children ≥4 (OR = 1.7, P = 0.046), and having children under 18 years. (OR = 2.1, P = 0.011) were significant independent risk factors for T2D. Conclusion The prevalence of FI in patients with T2D was significantly higher compared to the controls. FI was an important risk factor for T2D, even after controlling for the potential confounders. Further studies are suggested.

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In this paper we present a model-predictive control (MPC) based approach for vehicle platooning in an urban traffic setting. Our primary goal is to demonstrate that vehicle platooning has the potential to significantly increase throughput at intersections, which can create bottlenecks in the traffic flow. To do so, our approach relies on vehicle connectivity: vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication. In particular, we introduce a customized V2V message set which features a velocity forecast, i.e. a prediction on the future velocity trajectory, which enables platooning vehicles to accurately maintain short following distances, thereby increasing throughput. Furthermore, V2I communication allows platoons to react immediately to changes in the state of nearby traffic lights, e.g. when the traffic phase becomes green, enabling additional gains in traffic efficiency. We present our design of the vehicle platooning system, and then evaluate performance by estimating the potential gains in terms of throughput using our results from simulation, as well as experiments conducted with real test vehicles on a closed track. Lastly, we briefly overview our demonstration of vehicle platooning on public roadways in Arcadia, CA.INDEX TERMS Vehicle platooning, traffic throughput, model predictive control.

We present the design of a safe Adaptive Cruise Control (ACC) which uses road grade and lead vehicle motion preview. The ACC controller is designed by using a Model Predictive Control (MPC) framework to optimize comfort, safety, energy-efficiency and speed tracking accuracy. Safety is achieved by computing a robust invariant terminal set. The paper presents a novel approach to compute such set which is less conservative than existing methods. The proposed controller ensures safe inter-vehicle spacing at all times despite changes in the road grade and uncertainty in the predicted motion of the lead vehicle. Simulation results compare the proposed controller with a controller that does not incorporate prior grade knowledge on two scenarios including car-following and autonomous intersection crossing. The results demonstrate the effectiveness of the proposed control algorithm.