Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv admin note: text overlap with arXiv:2209.1186

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct numerous experiments on the single modality of radar and camera, as well as the fused modalities. Results demonstrate that 4D radar-camera fusion can considerably enhance the robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io

Development of a heavy metal sensing boat for automatic analysis in natural waters utilizing anodic stripping voltammetry

Altres ajuts: CERCA Programme/Generalitat de CatalunyaDetermination of the levels of heavy metal ions would support assessment of sources and pathways of water pollution. However, traditional spatial assessment by manual sampling and off-site detection in the laboratory is expensive and time-consuming and requires trained personnel. Aiming to fill the gap between on-site automatic approaches and laboratory techniques, we developed an autonomous sensing boat for on-site heavy metal detection using square-wave anodic stripping voltammetry. A fluidic sensing system was developed to integrate into the boat as the critical sensing component and could detect ≤1 μg/L Pb, ≤6 μg/L Cu, and ≤71 μg/L Cd simultaneously in the laboratory. Once its integration was completed, the autonomous sensing boat was tested in the field, demonstrating its ability to distinguish the highest concentration of Pb in an effluent of a galena-enriched mine compared to those at other sites in the stream (Osor Stream, Girona, Spain)

Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPI’s Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last year’s event-winning boat

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPIs Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last years event-winning boat

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPIÂs Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last yearÂs event-winning boat

Sailbot 2017-2018

The goal of this MQP was to build and program a robot capable of competing in the 2018 International Robotic Sailing Competition (IRSC), also known as Sailbot. This project utilized existing research on control and design of autonomous sailboats, and built on lessons learned from the last two years of WPIs Sailbot entries. The final product of this MQP was a more reliable, easier to control, and more innovative design than last years event-winning boat