Large Scale Particle Image Velocimetry (LSPIV) has been successfully used in the field to gather water surface flow data, which is critical to understanding complex geomorphic, hydrologic, and ecological river processes. Its success, however, depends on lighting conditions and adequate flow seeding with trackable debris or visual features, that can be hampered by environmental disturbances such as wind or wildlife. Instead, this research proposes augmenting traditional LSPIV methods by incorporating autonomous surface vehicles (ASVs) as pieces of actuated debris. This addressses the shortcomings of artificial seeding with tracers that may not be recoverable and impractical. Our method uses initial image velocimetry flow estimates to guide an ASV to sparsely seeded or poorly illuminated regions within the survey area. There it can be set adrift and tracked from above to capture additional measurements to improve the surface flow field reconstruction. We compare conventional techniques with our augmented LSPIV system in simulation and in field tests. The results showcase performance and capability enhancements ASVs can provide.

This research addresses the problem of planning efficient paths for agents through flow fields in small real-world domains where vehicle dynamics and environmental uncertainty can significantly affect the optimality of a path. In particular, we consider the task of planning routes for small autonomous airboats deployed in various river domains so as to best take advantage of water currents to save energy and time. Existing planning techniques for flow fields were implemented on our airboat platform and evaluated on these domains with current models developed from the data gathered using a Nortek AD2CP-Glider acoustic doppler current profiler. The real-world performance of these algorithms were compared to theoretical estimates and several modifications are suggested to improve their performance in specific domains.

In this paper, we outline a low cost multi-robot autonomous platform for a broad set of applications including water quality monitoring, flood disaster mitigation and depth buoy verification. By working cooperatively, fleets of vessels can cover large areas that would otherwise be impractical, time consuming and prohibitively expensive to traverse by a single vessel. We describe the hardware design, control infrastructure, and software architecture of the system, while additionally presenting experimental results from several field trials. Further, we discuss our initial efforts towards developing our system for water quality monitoring, in which a team of watercraft equipped with specialized sensors autonomously samples the physical quantity being measured and provides online situational awareness to the operator regarding water quality in the observed area. From canals in New York to volcanic lakes in the Philippines, our vessels have been tested in diverse marine environments and the results obtained from initial experiments in these domains are also discussed.

This paper presents a visual obstacle avoidance system for low-cost autonomous watercraft in riverine environments, such as lakes and rivers. Each watercraft is equipped with a smartphone which offers a single source of perceptual sensing, via a monocular camera. To achieve autonomous navigation in riverine environments, watercraft must overcome the challenges of limited sensing, low computational resources and visually noisy dynamic environments. We present an optical-flow based system that is robust to visual noise, predominantly in the form of water reflections, and provides local reactive visual obstacle avoidance. Through extensive field testing, we show that this system achieves high performance visual obstacle avoidance.

This paper addresses the problem of using a fleet of autonomous watercraft to create models of various water quality parameters in complex environments using intelligent sampling algorithms. Maps depicting the spatial variation of these parameters can help researchers understand how certain ecological processes work and in turn help reduce the negative impact of human activities on the environment. In our domain of interest, it is infeasible to exhaustively sample the field to obtain statistically significant results. This problem is pertinent to autonomous water sampling where hysteresis in sensors causes delay in obtaining accurate measurements across a large field. In this paper, we present several different approaches to sampling with cooperative vehicles to quickly build accurate models of the environment. In addition, we describe a novel filter and a specialized planner that uses the gradient of sensor measurements to compensate for hysteresis while ensuring a fast sampling process. We validate the algorithms using results from both simulation and field experiments with four autonomous airboats measuring temperature and dissolved oxygen in a lake.

Multi-robot systems (MRS) have received a great deal of attention recently due to their potential to address complex distributed tasks such as environmental monitoring, search and rescue, agriculture, and security [3, 4, 5, 1, 2]. One specific type of multi-robot system that has significant near term promise is fleets of autonomous watercraft for applications such as flood response, water monitoring and bathymetry. Small watercraft are an attractive option for real world multi-robot systems because some of the most critical robotic problems are minimized on water - movement is relatively simple and dangers are relatively low.