Modeling and Control for Vision Based Rear Wheel Drive Robot and Solving Indoor SLAM Problem Using LIDAR

abstract: To achieve the ambitious long-term goal of a feet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses several critical modeling, design, control objectives for rear-wheel drive ground vehicles. Toward this ambitious goal, several critical objectives are addressed. One central objective of the thesis was to show how to build low-cost multi-capability robot platform that can be used for conducting FAME research. A TFC-KIT car chassis was augmented to provide a suite of substantive capabilities. The augmented vehicle (FreeSLAM Robot) costs less than $500 but offers the capability of commercially available vehicles costing over$2000. All demonstrations presented involve rear-wheel drive FreeSLAM robot. The following summarizes the key hardware demonstrations presented and analyzed: (1)Cruise (v, ) control along a line, (2) Cruise (v, ) control along a curve, (3) Planar (x, y) Cartesian Stabilization for rear wheel drive vehicle, (4) Finish the track with camera pan tilt structure in minimum time, (5) Finish the track without camera pan tilt structure in minimum time, (6) Vision based tracking performance with different cruise speed vx, (7) Vision based tracking performance with different camera fixed look-ahead distance L, (8) Vision based tracking performance with different delay Td from vision subsystem, (9) Manually remote controlled robot to perform indoor SLAM, (10) Autonomously line guided robot to perform indoor SLAM. For most cases, hardware data is compared with, and corroborated by, model based simulation data. In short, the thesis uses low-cost self-designed rear-wheel drive robot to demonstrate many capabilities that are critical in order to reach the longer-term FAME goal.Dissertation/ThesisDefense PresentationMasters Thesis Electrical Engineering 201

Adaptive Navigation Utilizing a Drone Cluster

The current infrastructure for drone piloting involves drones flying in a predetermined path to find an object, source, or target with a known location. Drones should be able to determine an optimal flight path mid-flight in order to find an undefined source or target given existing parameters that can be analyzed from incoming sensor data. However, a framework for such a feat is almost non-existent. Our project aims to build such a framework that allows for the control of and communication among multiple drones, called a cluster, and allows for in-flight analysis and processing of sensor information to determine how to progress towards finding a source. This second objective of adaptive navigation is when the cluster of drones report their sensor data to a central ground station, allowing for a gradient calculation to be executed. The cluster of drones can follow this gradient to progress towards a source with a previously unknown specific location or path to the location

Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

MSFC Skylab lessons learned

Key lessons learned during the Skylab Program that could have impact on on-going and future programs are presented. They present early and sometimes subjective opinions; however, they give insights into key areas of concern. These experiences from a complex space program management and space flight serve as an early assessment to provide the most advantage to programs underway. References to other more detailed reports are provided

MSFC Skylab program engineering and integration

A technical history and managerial critique of the MSFC role in the Skylab program is presented. The George C. Marshall Space Flight Center had primary hardware development responsibility for the Saturn Workshop Modules and many of the designated experiments in addition to the system integration responsibility for the entire Skylab Orbital Cluster. The report also includes recommendations and conclusions applicable to hardware design, test program philosophy and performance, and program management techniques with potential application to future programs

Towards Autonomous Driving at the Limit of Friction

Autonomous vehicles have become a reality, many vehicles have implemented some features to allow partial or full autonomy; however, full autonomous driving near the limit of friction still presents many obstacles, especially near the limit of friction. Autonomous test vehicles are expensive to build and maintain, running the vehicles usually requires highly specialized training, and testing can be dangerous. Research has shown that small sized scaled vehicles may be used as an alternative to full size vehicle testing. The first part of this thesis presents the construction of a 1=5th scaled vehicle testbed. This testbed is inexpensive to construct, easy to maintain, and safe to test compared to full size vehicles. In the linear region, the dynamic response of the tires also closely mimics full size tires and the Dugoff tire model. The small sized testbed is therefore an ideal alternative to full size vehicles. The interaction between the road and the tires remains a challenge to estimate, but a requirement for eff ective control. Tire dynamics are highly non-linear, and are dependent on many variables. Tire slip angles are di fficult to estimate without expensive sensors set-up. Many linear and non-linear estimation methods have been developed to tackle this problem, but each having its limitations. The second part of the thesis presents a method for slip angle estimation, and proposes an observer design which integrates a linear component with the Dugoff tire model and a pneumatic trail estimator. This design is fast to operate, and does not require expensive sensors. With the addition of the pneumatic trail block, accurate slip angles can be obtained in the tires linear and saturation regions equally. Controlling near the limit of friction requires consistently accurate tire states, which is di fficult to achieve with slip angles. With the margin of error under a degrees, a slight error in slip angle estimates while operating at the limit of friction may result in loss of control. The final contribution of this thesis proposes a simpli ed feedforward lateral controller based on the concept of Centre of Percussion (COP), and a longitudinal controller that operates based on lateral acceleration. This control scheme avoids using slip angles, but still pushes the vehicle performance to the limit of friction. The architecture is validated in high fi delity simulations