Evolution of Neural Networks for Helicopter Control: Why Modularity Matters

The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so

Transportation mode recognition fusing wearable motion, sound and vision sensors

We present the first work that investigates the potential of improving the performance of transportation mode recognition through fusing multimodal data from wearable sensors: motion, sound and vision. We first train three independent deep neural network (DNN) classifiers, which work with the three types of sensors, respectively. We then propose two schemes that fuse the classification results from the three mono-modal classifiers. The first scheme makes an ensemble decision with fixed rules including Sum, Product, Majority Voting, and Borda Count. The second scheme is an adaptive fuser built as another classifier (including Naive Bayes, Decision Tree, Random Forest and Neural Network) that learns enhanced predictions by combining the outputs from the three mono-modal classifiers. We verify the advantage of the proposed method with the state-of-the-art Sussex-Huawei Locomotion and Transportation (SHL) dataset recognizing the eight transportation activities: Still, Walk, Run, Bike, Bus, Car, Train and Subway. We achieve F1 scores of 79.4%, 82.1% and 72.8% with the mono-modal motion, sound and vision classifiers, respectively. The F1 score is remarkably improved to 94.5% and 95.5% by the two data fusion schemes, respectively. The recognition performance can be further improved with a post-processing scheme that exploits the temporal continuity of transportation. When assessing generalization of the model to unseen data, we show that while performance is reduced - as expected - for each individual classifier, the benefits of fusion are retained with performance improved by 15 percentage points. Besides the actual performance increase, this work, most importantly, opens up the possibility for dynamically fusing modalities to achieve distinct power-performance trade-off at run time

Effects of Connected Automated Vehicles on Traffic Flow

In this dissertation we provide a comprehensive framework for evaluating the merits of wireless vehicle-to-vehicle (V2V) communication on traffic. In particular we focus on mixed traffic scenarios that will dominate highways in the next several decades. Such mixed traffic primarily contains conventional human driven vehicles, however also includes connected human-driven vehicles, automated vehicles, and connected automated vehicles. Connected human-driven vehicles are human-operated vehicles that are able to send and receive messages using V2V. Automated vehicles rely on an internal computer (rather than a human) to process information from sensors such as cameras or radars to control their motion. Finally connected automated vehicles are automated vehicles that use information received from V2V communication in addition to sensory information for controlling their motion. Our framework is based on developing a prototype connected automated vehicle and investigating its effects on traffic patterns amongst human driven vehicles. We first establish an experimental procedure and criteria for tuning the connected automated vehicle's controller to follow a connected human-driven vehicle at a desired distance. We then showcase an experimental configuration that allows us to observe traffic patterns in a three-car connected vehicle network, where our connected automated vehicle interacts with two connected human-driven vehicles. These experiments demonstrate the effectiveness of connected automated vehicles using beyond-line-of-sight information in promoting smooth traffic flow in a mixed traffic environment. To investigate the effects of connected automated vehicles for large networks, we first focus on simple car-following models without communication, actuation or human reaction delay. For these models we are able to analytically characterize the traffic patterns occurring in human driven traffic at various densities, as well as show that connected automated vehicles can indeed mitigate congestion and promote stable uniform flow of traffic. By exploiting the cyclic symmetry of the governing equations, we rigorously show that the results hold for arbitrarily large connected vehicle networks, and also that the feedback to long-range information in connected automated vehicles should be carefully chosen to ensure the benefit to traffic flow. Lastly we use simulations to investigate large connected vehicle networks, where delays, nonlinearities, wireless communication delay, and eclectic driving dynamics are considered. We use these simulations to demonstrate that indeed the information from beyond-line-of-sight is the key feature that allows the connected automated vehicles to bring significant benefits to traffic. Finally, we conduct penetration studies to quantify the extent to which connected automated vehicles may benefit traffic at partial penetrations, and discuss the implications of this study on the current competing wireless V2X communication technologies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153372/1/avediska_1.pd

Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

DESIGN OF AVIONICS AND CONTROLLERS FOR AUTONOMOUS TAKEOFF, HOVER AND LANDING OF A MINI-TANDEM HELICOPTER

Robotics autonomy is an active research area these days and promises very useful applications. A lot of research has been carried out on Vertical Takeoff and Landing (VTOL) vehicles especially single rotor small scale helicopters. This thesis focuses on a small scale twin rotor helicopter. These helicopters are more useful because of their power efficiency, scalability, long range of center of gravity, shorter blades and most importantly their "all lift" feature. By "all lift" we mean that unlike single rotor helicopters where tail rotor's power is wasted just to cancel the torque of the main rotor both of its rotors are used for generating lift. This makes twin rotors ideal for lifting heavy weights. This thesis considers avionics systems and the controllers development for a twin rotor. It involves electronic component selection and integration, software development, system identification and design of zero rate compensators. The compensators designed are responsible for autonomous take-off, hover and landing of this unmanned aerial vehicle (UAV). Both time and frequency domain system identification approaches were evaluated and a selection was made based on hardware limitations. A systematic approach is developed to demonstrate that a rapid prototyping UAV can be designed from cheap off-the-shelf components that are readily available and functionally compatible. At the end some modifications to existing mechanical structure are proposed for more robust outdoor hovering

Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications