The range and endurance of an unmanned aerial system operating nominally in an outdoor environment depends upon the available power and environmental factors like the magnitude and direction of the prevailing wind. This paper focuses on the development of semi-analytical approaches to computing the range and endurance of battery-powered multi-copter unmanned aerial system under varying wind conditions. The analytically derived range is verified against a comprehensive unmanned aerial system simulation which includes experimentally validated elements such as the propulsion system and electric power consumption modules. It is shown that the analytical approach yields the range maps in close agreement with the simulation results.
We present a novel sensor calibration methodology that is suited to an evidence theoretic Unmanned Ground Vehicle (UGV) localization system. The proposed procedure for sensor calibration employs a series of designed experiments with the objective of creating parametric calibration models and forming a mass assignment table for a Dempster-Shafer belief system. Sensors calibrated include custom built magnetic encoders positioned at the rear wheels of the UGV, an accelerometer, a solid-state rate-gyro, a digital compass, and a Global Positioning System (GPS). The estimated parameters together with a mass assignment table are presented. This table is created for the GPS unit based on the factors that significantly impact the accuracy of the readings using an experimental procedure. We conclude with a brief summary of the main results.
In this paper, we present a novel evidence theoretic fusion filter, and its application to the Unmanned Ground Vehicle (UGV) localization problem. The various components of the sensor fusion framework such as the adaptive pre-processing unit, the evidence extraction and combination unit, and the extended Kalman filter are described in detail. The crux of this architecture is the evidence extraction and combination unit that employs a twopronged approach, one to switch between parametric models, and another to adaptively vary the measurement noise covariance matrix. The process of evidence extraction using fuzzy-type or rule-based techniques, and their subsequent combination using the Dempster's rule for combination are detailed. An experiment is conducted to demonstrate the merits of this UGV localization approach. Finally, we conclude with a brief summary of the results.
Schools throughout the United States have adopted zero‐tolerance strategies to address school discipline. These policies have resulted in a significant increase in suspensions and expulsions. The placement of police on campus has exacerbated the problem by adding arrests and referrals to juvenile court as a disciplinary tool. This article discusses the origin of zero tolerance and its negative effects on school safety and graduation rates. This article examines three jurisdictions and their application of a collaborative model using judicial leadership to convene stakeholders resulting in written protocols to reduce school arrests and suspensions and developing alternatives that have produced better outcomes for students, the school, and the community.
Keypoints
Reduce arrest of students for minor offenses
Develop alternatives to suspension, expulsion, and arrests
Create system of care targeting chronically disruptive student for behavior improvement
Improve school safety using a Positive Student Engage Model for Campus Police
Improve school climate
Increase graduation rates
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