An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

Design, Testing and Evaluation of Robotic Mechanisms and Systems for Environmental Monitoring and Interaction

Unmanned Aerial Vehicles (UAVs) have significantly lowered the cost of remote aerial data collection. The next generation of UAVs, however, will transform the way that scientists and practitioners interact with the environment. In this thesis, we address the challenges of flying low over water to collect water samples and temperature data. We also develop a system that allows UAVs to ignite prescribed fires. Specifically, this thesis contributes a new peristaltic pump designed for use on a UAV for collecting water samples from up to 3m depth and capable of pumping over 6m above the water. Next, temperature sensors and their deployment on UAVs, which have successfully created a 3D thermal structure map of a lake, contributes to mobile sensors. A sub-surface sampler, the “Waterbug” which can sample from 10m deep and vary buoyancy for longer in-situ analysis contributes to robotics and mobile sensors. Finally, we designed and built an Unmanned Aerial System for Fire Fighting (UAS-FF), which successfully ignited over 150 acres of prescribed fire during two field tests and is the first autonomous robot system for this application. Advisers: Carrick Detweiler and Carl Nelso

Evaluation of the utility and performance of an autonomous surface vehicle for mobile monitoring of waterborne biochemical agents

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies