Urban Air Mobility Market Study

The Booz Allen Team explored market size and potential barriers to Urban Air Mobility (UAM) by focusing on three potential markets – Airport Shuttle, Air Taxi, and Air Ambulance. We found that the Airport Shuttle and Air Taxi markets are viable, with a significant total available market value in the U.S. of $500 billion, for a fully unconstrained scenario. In this unconstrained best-case scenario, passengers would have the ability to access and fly a UAM at any time, from any location to any destination, without being hindered by constraints such as weather, infrastructure, or traffic volume. Significant legal and regulatory, weather, certification, public perception, and infrastructure constraints exist, which reduce the market potential for these applications to only about 0.5$2.5 billion, in the near term. However, we determined that these constraints can be addressed through ongoing intra-governmental partnerships, government and industry collaboration, strong industry commitment, and existing legal and regulatory enablers. We found that the Air Ambulance market is not a viable market if served by electric vertical takeoff and landing (eVTOL) vehicles due to technology constraints but may potentially be viable if a hybrid VTOL aircraft are utilized

Renewable Energy Powered Autonomous Smart Ocean Surface Vehicles (REASOSE)

The REASOSE is not just an Ocean surface vehicle, its poly-type smart autonomous propulsion which eliminates the limitations of existing surface vehicles (remotely operated). The renewable energy source always proved to be abundance of availability in the environment, since the power created through renewable source with loss is engineering acceptance which can immobilise the vehicle. But REASOSE is a unique vehicle with poly-type propulsion incorporated with different renewable sources from the environment which furnishes the consistency of the vehicle inevitable. The REASOSE is a smart intelligent system of vehicle that autonomously switch over to the efficient propulsion as per the availability and in kind of any hindrances the vehicle acts smartly and reaches its destination contiguously. The proposed project novelty is not only stick to a line, the proposed vehicle serves to be change over for versatile applications, the vehicle will be incorporated with high definition live transmitted camera serves for coastal surveillance, deep sea monitoring and so on. The integrated CTD, ADCP and other oceanographic sensors can be a changeover in data collection at different area at required region and time. The stack-up space provides the transportation during unconditional or conditional mode of cargo transfer to required destination

Renewable Energy Powered Autonomous Smart Ocean Surface Vehicles (REASOSE)

The REASOSE is not just an Ocean surface vehicle, its poly-type smart autonomous propulsion which eliminates the limitations of existing surface vehicles (remotely operated). The renewable energy source always proved to be abundance of availability in the environment, since the power created through renewable source with loss is engineering acceptance which can immobilise the vehicle. But REASOSE is a unique vehicle with poly-type propulsion incorporated with different renewable sources from the environment which furnishes the consistency of the vehicle inevitable. The REASOSE is a smart intelligent system of vehicle that autonomously switch over to the efficient propulsion as per the availability and in kind of any hindrances the vehicle acts smartly and reaches its destination contiguously. The proposed project novelty is not only stick to a line, the proposed vehicle serves to be change over for versatile applications, the vehicle will be incorporated with high definition live transmitted camera serves for coastal surveillance, deep sea monitoring and so on. The integrated CTD, ADCP and other oceanographic sensors can be a changeover in data collection at different area at required region and time. The stack-up space provides the transportation during unconditional or conditional mode of cargo transfer to required destination

Technology challenges of stealth unmanned combat aerial vehicles

The ever-changing battlefield environment, as well as the emergence of global command and control architectures currently used by armed forces around the globe, requires the use of robust and adaptive technologies integrated into a reliable platform. Unmanned Combat Aerial Vehicles (UCAVs) aim to integrate such advanced technologies while also increasing the tactical capabilities of combat aircraft. This paper provides a summary of the technical and operational design challenges specific to UCAVs, focusing on high-performance, and stealth designs. After a brief historical overview, the main technology demonstrator programmes currently under development are presented. The key technologies affecting UCAV design are identified and discussed. Finally, this paper briefly presents the main issues related to airworthiness, navigation, and ethical concerns behind UAV/UCAV operations

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Team MIT Urban Challenge Technical Report

This technical report describes Team MITs approach to theDARPA Urban Challenge. We have developed a novel strategy forusing many inexpensive sensors, mounted on the vehicle periphery,and calibrated with a new cross-­modal calibrationtechnique. Lidar, camera, and radar data streams are processedusing an innovative, locally smooth state representation thatprovides robust perception for real­ time autonomous control. Aresilient planning and control architecture has been developedfor driving in traffic, comprised of an innovative combination ofwell­proven algorithms for mission planning, situationalplanning, situational interpretation, and trajectory control. These innovations are being incorporated in two new roboticvehicles equipped for autonomous driving in urban environments,with extensive testing on a DARPA site visit course. Experimentalresults demonstrate all basic navigation and some basic trafficbehaviors, including unoccupied autonomous driving, lanefollowing using pure-­pursuit control and our local frameperception strategy, obstacle avoidance using kino-­dynamic RRTpath planning, U-­turns, and precedence evaluation amongst othercars at intersections using our situational interpreter. We areworking to extend these approaches to advanced navigation andtraffic scenarios

Dynamic modeling and optimal control of a positive buoyancy diving autonomous vehicle

The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value