Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

The U.S. Army: A Learning Organization

Abstract: Peter M. Senge, a senior lecturer at Massachusetts Institute of Technology (MIT), first introduced his learning organization concept in the early 1990’s with the publishing of The Fifth Discipline book, which has since been revised during 2006. A ‘learning organization’ focused on the development of every member with superior performance in service of that organization’s purpose. The more the organization’s members increase their ability to learn collaboratively, the more they can accomplish the higher performance, which can then effectively and positively change their organization. Learning organizations can include corporation, schools, hospitals, non-for-profits, and government agencies – basically, any organization where people are placed together to accomplish a common goal, which they could not have created on their own. Organizational learning has five lifelong disciplines: systems thinking, personal mastery, mental models, shared vision, and team learning. This paper will introduce some of the several aspects of the learning organization in the U.S. Army based on the current fight against the war on terror.

Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming

Abstract: A mixed aerial and ground robot team, which includes both unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs), is widely used for disaster rescue, social security, precision agriculture, and military missions. However, team capability and corresponding configuration vary since robots have different motion speeds, perceiving ranges, reaching areas, and resilient capabilities to the dynamic environment. Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability. To address this challenge for effective mixed ground and aerial teaming, this paper developed a novel teaming method, proficiency aware multi-agent deep reinforcement learning (Mix-RL), to guide ground and aerial cooperation by considering the best alignments between robot capabilities, task requirements, and environment conditions. Mix-RL largely exploits robot capabilities while being aware of the adaption of robot capabilities to task requirements and environment conditions. Mix-RL's effectiveness in guiding mixed teaming was validated with the task “social security for criminal vehicle tracking”.

Comment on “The Load Distribution on a Polynomial Mean Line and the Converse”

Abstract: I am grateful to Mr. Llewelyn for pointing out the slip in equation (3) of reference 1. In considering a particular case of the polynomial camber line I did have to evaluate a number of integrals of the kind but the process is not as laborious as perhaps Mr. Llewelyn implies for there is a simple recurrence relation between integrals of this kind.