Light Detection and Ranging (LiDAR)
The Velodyne LiDAR PUCK LITE or PUCK VLP-16 is a Light Detection and Ranging (LiDAR) sensor, funded by Student Technology Fees (STF). It is mounted onto Unmanned Aerial Vehicle (UAV) platforms, where the LiDAR sensor uses lasers to create high-resolution, 3D aerial imagery of the terrain, vegetation and animal life. This has applications in engineering, environmental sciences, geospatial analysis, and many others. The access to the sensor allows students to research with the sensor's capabilities and study the changes through point cloud data.
The Visual Anchoring Project aims to autonomously establish an orbit about a target of interest using vision as the primary sensor. This would improve the capability for autonomous UAVs to operate in GPS denied environments. The Visual Anchoring team has developed an autonomous control algorithm to establish an orbit with a fixed wing UAV and implemented it on Pixhawk flight controller. In addition, the team has developed a vision system that is able to track a visually distinct target in real time and estimate the distance between the aircraft and the target. The team is currently conducting flight tests to determine the accuracy of the vision system.
This project proposes a novel approach to processing and utilizing aerial imagery and data for mapping. With the advancement of unmanned aerial systems (UAS), airborne data has gained considerable momentum in recent decades revolutionizing methodologies of geographic research, law enforcement, agriculture and mapping. Specifically, advances in consumer electronics significantly eased access to UAS and development in parallel computing has enabled machine learning on large data sets. This paper proposed an alternative method to the current practice of processing airborne data sets. Instead of obtaining aerial imagery of an object and merely presenting those objects as pixels on a map, the proposed method uses machine learning techniques to recognize the object, assigns parameters to the object and renders the object in a fashion that is most efficient and understandable to the terminal user. The object is no longer be merely a set of pixels but a list of classified objects with associated parameters, a process analogous to cognitive processes. This paper explores the methodology for collecting airborne data sets for this process and presents several use cases of this using flight test data. Hence one data set could enable simulation of changes in the environment, and could be rendered in multiple interfaces, photorealistic or analytical.
MicaSenseAerial Pointing and Stabilization System
The MicaSenseAerial Pointing and Stabilization System (MAPSS) is intended to be a modular attachable gimbal apparatus which keeps the attached sensors facing nadir. It is the intent of the MAPSS project to develop a working prototype gimbal with mounting points for RedEdgeand one other accessory sensor. Categorizing the tolerable vibrations while keeping image quality high is also part of the project. If successful, MAPSS would remove the need for potentially dangerous homemade mounting hardware by the MicaSense customer base while increasing the quality of flight missions through the nadir-facing controls.
Characterizing UAS Wireless Communication Links
Our team is working on a Joint Center for Aerospace Technology Innovation project with the Fundamentals of Networking Lab in the Department of Electrical Engineering to test the integration of software defined radios (SDRs) with unmanned aerial vehicles. Currently, we are performing flight tests to gather data to be able to characterize the link quality between a UAV based SDR and a ground SDR. At the end of the project, we aim to release a database that contains link quality and UAV telemetry data for public use.
Transponder Based Position Information System (TRAPIS)
This TRAPIS project aims to combine position data from multiple sources into a consistent position estimate. This will further allow determination of aircraft position in GPS denied environments. In addition, secondary software applications will be able to utilize the functionalities of UW TRAPIS to drive other autonomy applications. The project has successfully used ANPC’s Local Area Multilateration System (LAMS) to navigate a UAV without any GPS data going directly to the plane. Currently, the AFSL is investigating and integrating other potential sources of position data.
Collision Warning System for Multiple Unmanned Aerial Systems
The FAA is scheduled to open the National Airspace System by 2015 for all unmanned aerial systems (UAS). To help with this transition, our team develops software and algorithms that provides situational awareness to UAS operators about potential conflicts. We are partnered with Insitu Inc. and funded by the Washington Joint Center for Aerospace Technology Innovation (JCATI). The collision awareness system will be integrated onto Insitu’s ground control station and be compatible with STANAG 4586 vehicles.
A major focus of current unmanned systems operations is assessing the inherent risk associated with a mission. Efforts to integrate unmanned systems into the national airspace require manufacturers be able to calculate the risk of a mission in terms of human safety. Threats to human safety from midair collisions and ground strikes are the focus of the risk model. The project’s intent is to assist in determining applications that leverage the strengths of current unmanned aircraft technology while mitigating the weaknesses so as to meet or exceed the safety and economic viability of manned aircraft. The validity of the risk model is demonstrated by comparison to historical data when available. The intended use of the tool is discussed and risk assessments are presented for several example scenarios. Resources for gathering the required information are surveyed and material is developed to aid a general audience in performing a risk assessment.
Many modern autonomous systems actually require significant human involvement. Often, the amount of human support and infrastructure required for these autonomous systems exceeds that of their manned counterparts. This work involves increasing both the tactical and strategic decision making capabilities of various autonomous systems. The application considered is the problem of searching for targets using a team of heterogeneous agents. The system maintains a grid-based world model which contains information about the probability of a target being located in any given cell of the map. Agents formulate control decisions for a fixed number of time steps using a modular algorithm that allows for capabilities and characteristics of individual agents to be encoded in several parameters. The resulting research patterns executed by the agents guarantee an exhaustive search of the map in the sense that all cells will be searched sufficiently to ensure that the probability of a target being located in any given cell is driven to zero. This system was simulated using high fidelity simulations with heterogeneous agents in complex and dynamic environments. After performing successfully in simulation, these algorithms were then verified and validated on a distributed human-in-the-loop simulator. This system allows a human operator to handle low level tasks such as state stabilization and signal tracking while preserving the contributions of the autonomous algorithm. Finally, flight test results are presented showing the benefits of augmenting a human system with these types of autonomous algorithms.
The objective of the work in this project is to design integrated control and communication algorithms that guarantee that a set of vehicles with differing data capabilities will conform to a specified spatial distribution. Unlike most coordinated control settings, we are not only interested in the question of formation control, but also the question of reliably providing the communication necessary to achieve the coordination. Each vehicle is equipped with physical devices that provide local information from sensing and global information from communication. Sensing devices are assumed to be lower cost in terms of power, computation, and range of operation, while communication devices are comparatively higher cost but with more information density and reconfigurability. While each vehicle is equipped with a communication device, not all vehicles will be allowed to use these devices due to resource limitations, environmental constraints such as bandwidth, and/or failure of the device. Such scenarios are typical of multiple autonomous aircraft in urban settings or multiple autonomous underwater vehicles in shipyards.
In this research, we investigate the problem of dynamic planning for a team of autonomous vehicles to cooperatively execute a set of assigned tasks. The emphasis is on the system architecture, planning algorithms and uncertainty management.
This research involves the integration of weather information from actual databases and forecast algorithms with evolutionary path planning techniques for long range autonomous flights. It addresses a 4-dimensional problem of space and time, requiring adaptation to account for unexpected changes in the environment. The objective is the planning of trajectories that balance fuel consumption, icing hazard avoidance and observation requirements, considering performance characteristics of the target vehicle.
This project focuses on using total magnetic intensity measurements to search and identify magnetic anomalies in a predetermined area. The challenge is to use noisy sensor measurements to identify and classify these anomalies. This concept is integrated with a centralized occupancy based map search idea to apply to a team of autonomous agents.
Maneuvering a single vehicle from one location to another is a common problem encountered by many autonomous systems. This problem becomes more complicated when an operator desires to maneuver multiple vehicles simultaneously in a coordinated fashion through a complex and potentially dynamic environment. This work investigates a modular control strategy which allows a group of vehicles, or swarm, to move in formation from a starting location to an ending location in space while navigating various obstacles and obstructions in a cluttered environment. The algorithm structure allows different aspects of the controller to be tuned and modified as desired for a specific task. High fidelity simulation is used to verify the algorithm before transitioning to flight test using multiple autonomous vehicles in real time.
Applications and scenarios of cooperative control of heterogeneous vehicle ensembles with communication constraints have been receiving increasing attention during the past several years. The driving technology behind this attention is the fact that in coordinated vehicle activities information about vehicle states, trajectory planning, and collected data must be shared among the group of vehicles, generally via data transmission. Current approaches to communication are ad hoc drawing on existing communications network technology. Specifically, techniques for coordinated control often assume continuous and uninterrupted transmission. Studies of static communication patterns among vehicles have shown that certain patterns prevent convergence to desired group behaviors. Dynamic communication patterns have been studied in a few limiting cases. When dynamic communication patterns are assumed to be periodic, different choices of pattern can be shown to either stabilize or destabilize group behaviors. Further results have been obtained for cases of random communication patterns for synchronous and instantaneous error-free communication. These results for random patterns, however, are either taken in a limit or do not allow for changing group membership and focus on analysis rather than synthesis.
In this research, we investigate adaptive real-time planning, control and communication architectures for on-board implementation for small USV platforms to give the vehicles greater autonomy, as well as enable cooperative operation with UAVs. To provide specific focus to this research,
In this project, we develop algorithms for fight path guidance and synchronous camera angles for UAVs to observe targets. The observation of the target is affected by the environment (e.g. sun angle, wind), maximum aircraft performance, and camera limits. Using minimal heuristics, a guidance law based on good helmsman behavior is developed and implemented, and stability of its integration with inner loops is assessed.
Non-linear Adaptive Control
This research focuses on the use of a direct Neural Network based adaptive control architecture that compensates for unknown plant nonlinearities in a feedback linearizing control framework. We analyze the system performance and stability based on Lyapunov theory. Robustness to actuator dynamics is a topic of interest.