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.

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".

Natural formulation of the random choice method for two–dimensional inert high–speed flow fields

Abstract: The random choice method has now been shown to be successfully extendible from the original one-dimensional unsteady formulation to inert high-speed flow fields which are steady and two-dimensional using Cartesian, axisymmetric and Lagrangian formulations. This paper deals with the description of a new implementation of the random choice method formulated for natural co-ordinates based on streamlines and normals. Comparisons between theoretical and computed results for several different physical configurations are presented.

Value-driven design for product families: a new approach for estimating value and a novel industry case study

Abstract: Advanced product platform and product family design methods are needed to define and optimize the value they bring to a company. Maximizing platform commonality and individual product performance often fails to realize the most valuable product family during optimization; however, few examples exist in the literature to explore these trade-offs. This paper introduces a novel industry case study to explore the differences between “traditional” multidisciplinary design optimization (MDO) and value-driven design (VDD) approaches to product family design. The case study involves a family of five commercially-available washing machines and integrates multidisciplinary analyses, simulations, mathematical models, and response surface models to obtain ratings for individual product attributes. These attributes are then aggregated into a value function for the product family using a novel approach to estimate sales volume and a demand sensitivity curve derived from publicly available data. We then formulate and solve a “traditional” MDO product family design problem using a multi-objective genetic algorithm to minimize performance deviation and a product family penalty function. A novel VDD formulation is then introduced and solved to maximize the net present value (NPV) for the firm producing the family of products. Visualization and comparison of the results illustrate that the “traditional” MDO formulation can find several promising solutions for the product family, but it fails to find solutions that maximize the value to the firm. The results also provide a benchmark for researchers to explore alternative value function formulations and solution approaches for product family design using the novel industry case study.