Increasing Reliability of a Small 2-Stroke Internal Combustion Engine for Dynamically Changing Attitudes

Remotely Piloted Aircraft (RPA) typically utilize commercial internal combustion engines (ICE) as their power sources. These engines are designed to run at sea level, but these aircraft are often pressed into service at higher altitudes where the performance characteristics deteriorate. A Brison 95cc two-stroke engine\u27s performance characteristics at altitude are investigated using a test facility that can measure these characteristics over a range of pressures and temperatures. With its stock carburetor at sea level static (SLS) conditions, the engine makes 5.5 peak horsepower (hp) and brake specific fuel consumption (BSFC) ranged from 1.2-4.0 lb/(hp-hr). At 10,000 feet conditions, the peak hp drops 40% while off peak hp conditions can see a drop of over 90%. In addition, the carburetor makes operating at high altitudes unreliable. To increase reliability, a throttle body fuel injection (TBI) system was installed on the engine in place of the carburetor. The fuel injection system matched carburetor peak power at SLS conditions while increasing power by as much as 90% at low RPM and high altitude operating conditions. BSFC is decreased to a consistent 1.0 to 1.2 lb/(hp-hr) across all operating conditions. Lastly, both reliability at high altitude and startup reliability are increased with the TBI system while eliminating the need for tuning by the end user

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

MODELING OF INNOVATIVE LIGHTER-THAN-AIR UAV FOR LOGISTICS, SURVEILLANCE AND RESCUE OPERATIONS

An unmanned aerial vehicle (UAV) is an aircraft that can operate without the presence of pilots, either through remote control or automated systems. The first part of the dissertation provides an overview of the various types of UAVs and their design features. The second section delves into specific experiences using UAVs as part of an automated monitoring system to identify potential problems such as pipeline leaks or equipment damage by conducting airborne surveys.Lighter-than-air UAVs, such as airships, can be used for various applications, from aerial photography, including surveying terrain, monitoring an area for security purposes and gathering information about weather patterns to surveillance. The third part reveals the applications of UAVs for assisting in search and rescue operations in disaster situations and transporting natural gas. Using PowerSim software, a model of airship behaviour was created to analyze the sprint-and-drift concept and study methods of increasing the operational time of airships while having a lower environmental impact when compared to a constantly switched-on engine. The analysis provided a reliable percentage of finding the victim during patrolling operations, although it did not account for victim behaviour. The study has also shown that airships may serve as a viable alternative to pipeline transportation for natural gas. The technology has the potential to revolutionize natural gas transportation, optimizing efficiency and reducing environmental impact. Additionally, airships have a unique advantage in accessing remote and otherwise inaccessible areas, providing significant benefits in the energy sector. The employment of this technology was studied to be effective in specific scenarios, and it will be worth continuing to study it for a positive impact on society and the environment

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

NASA Tech Briefs, December 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Architectures for online simulation-based inference applied to robot motion planning

Robotic systems have enjoyed significant adoption in industrial and field applications in structured environments, where clear specifications of the task and observations are available. Deploying robots in unstructured and dynamic environments remains a challenge, being addressed through emerging advances in machine learning. The key open issues in this area include the difficulty of achieving coverage of all factors of variation in the domain of interest, satisfying safety constraints, etc. One tool that has played a crucial role in addressing these issues is simulation - which is used to generate data, and sometimes as a world representation within the decision-making loop. When physical simulation modules are used in this way, a number of computational problems arise. Firstly, a suitable simulation representation and fidelity is required for the specific task of interest. Secondly, we need to perform parameter inference of physical variables being used in the simulation models. Thirdly, there is the need for data assimilation, which must be achieved in real-time if the resulting model is to be used within the online decision-making loop. These are the motivating problems for this thesis. In the first section of the thesis, we tackle the inference problem with respect to a fluid simulation model, where a sensorised UAV performs path planning with the objective of acquiring data including gas concentration/identity and IMU-based wind estimation readings. The task for the UAV is to localise the source of a gas leak, while accommodating the subsequent dispersion of the gas in windy conditions. We present a formulation of this problem that allows us to perform online and real-time active inference efficiently through problem-specific simplifications. In the second section of the thesis, we explore the problem of robot motion planning when the true state is not fully observable, and actions influence how much of the state is subsequently observed. This is motivated by the practical problem of a robot performing suction in the surgical automation setting. The objective is the efficient removal of liquid while respecting a safety constraint - to not touch the underlying tissue if possible. If the problem were represented in full generality, as one of planning under uncertainty and hidden state, it could be hard to find computationally efficient solutions. Once again, we make problem-specific simplifications. Crucially, instead of reasoning in general about fluid flows and arbitrary surfaces, we exploit the observations that the decision can be informed by the contour tree skeleton of the volume, and the configurations in which the fluid would come to rest if unperturbed. This allows us to address the problem as one of iterative shortest path computation, whose costs are informed by a model estimating the shape of the underlying surface. In the third and final section of the thesis, we propose a model for real-time parameter estimation directly from raw pixel observations. Through the use of a Variational Recurrent Neural Network model, where the latent space is further structured by penalising for fit to data from a physical simulation, we devise an efficient online inference scheme. This is first shown in the context of a representative dynamic manipulation task for a robot. This task involves reasoning about a bouncing ball that it must catch – using as input the raw video from an environment-mounted camera and accommodating noise and variations in the object and environmental conditions. We then show that the same architecture lends itself to solving inference problems involving more complex dynamics, by applying this to measurement inversion of ultrafast X-Ray scattering data to infer molecular geometry