Reactive evolutionary path planning for autonomous surface vehicles in lake environments.

Autonomous Surface Vehicles (ASVs) have found a lot of promising applications in aquatic environments, i.e., sea, lakes, rivers, etc. They can be used for applications of paramount importance, such as environmental monitoring of water resources, and for bathymetry to study the characteristics of the basing of a lake/sea or for surveillance in patrol missions, among others. These vehicles can be built with smaller dimensions when compared to regular ships since they do not need an on-board crew for operation. However, they do require at least a telemetry control as well as certain intelligence for making decisions and responding to changing scenarios. Water resources are very important in Paraguay since they provide fresh water for its inhabitants and they are crucial for the main economic activities such as agriculture and cattle raising. Furthermore, they are natural borders with the surrounding countries, and consequently the main transportation route for importing/exporting products. In fact, Paraguay is the third country in the world with the largest fleet of barges after USA and China. Thus, maintaining and monitoring the environmental conditions of these resources is key in the development of the country. This work is focused on the maintenance and monitoring of the greatest lake of the country called Ypacarai Lake. In recent years, the quality of its water has been seriously degraded due to the pollution caused by the low control of the dumping of waste thrown into the Lake. Since it is also a national icon, the government of Paraguay has put a lot of effort in recovering water quality of the Lake. As a result, it is monitored periodically but using manual procedures. Therefore, the primary objective of this work is to develop these monitoring tasks autonomously by means of an ASV with a suitable path planning strategy. Path planning is an active research area in robotics. A particular case is the Coverage Path Planning (CPP) problem, where an algorithm should find a path that achieves the best coverage of the target region to be monitored. This work mainly studies the global CPP, which returns a suitable path considering the initial conditions of the environment where the vehicle moves. The first contribution of this thesis is the modeling of the CPP using Hamiltonian Circuits (HCs) and Eulerian Circuits (ECs). Therefore, a graph adapted to the Ypacarai Lake is created by using a network of wireless beacons located at the shore of the lake, so that they can be used as data exchange points between a control center and the ASV, and also as waypoints. Regarding the proposed modeling, HCs and ECs are paths that begin and end at the same point. Therefore, the ASV travels across a given graph that is defined by a set of wireless beacons. The main difference between HC and EC is that a HC is a tour that visits each vertex only once while EC visits each edge only once. Finding optimal HCs or ECs that minimize the total distance traveled by the ASV are very complex problems known as NP-complete. To solve such problems, a meta-heuristic algorithm can be a suitable approach since they provide quasi-optimal solutions in a reasonable time. In this work, a GA (Genetic Algorithm) approach is proposed and tested. First, an evaluation of the performance of the algorithm with different values of its hyper-parameters has been carried out. Second, the proposed approach has been compared to other approaches such as randomized and greedy algorithms. Third, a thorough comparison between the performance of HC and EC based approaches is presented. The simulation results show that EC-based approach outperforms the HC-based approach almost 2% which in terms of the Lake size is about 1.4 km2 or 140 ha (hectares). Therefore, it has been demonstrated that the modeling of the problem as an Eulerian graph provides better results. Furthermore, it has been investigated the relationship between the number of beacons to be visited and the distance traveled by the ASV in the EC-based approach. Findings indicate that there is a quasi-lineal relationship between the number of beacons and the distance traveled. The second contribution of this work is the development of an on-line learning strategy using the same model but considering dynamic contamination events in the Lake. Dynamic events mean the appearance and evolution of an algae bloom, which is a strong indicator of the degradation of the lake. The strategy is divided into two-phases, the initial exploration phase to discover the presence of the algae bloom and next the intensification phase to focus on the region where the contamination event is detected. This intensification effect is achieved by modifying the beacon-based graph, reducing the number of vertices and selecting those that are closer to the region of interest. The simulation results reveal that the proposed strategy detects two events and monitors them, keeping a high level of coverage while minimizing the distance traveled by the ASV. The proposed scheme is a reactive path planning that adapts to the environmental conditions. This scheme makes decisions in an autonomous way and it switches from the exploratory phase to the intensification phase depending on the external conditions, leading to a variable granularity in the monitoring task. Therefore, there is a balance between coverage and the energy consumed by the ASV. The main benefits obtained from the second contribution includes a better monitoring in the quality of water and control of waste dumping, and the possibility to predict the appearance of algae-bloom from the collected environmental data

Drones count wildlife more accurately and precisely than humans

Human activities are creating environmental conditions that pose threats and present opportunities for wildlife. In turn, this creates challenges for conservation managers. Some species have benefited from anthropogenic actions. For example, many invasive species profit from human‐assisted dispersal (Banks, Paini, Bayliss, & Hodda, 2015; Hulme, 2009), and mesopredators may thrive following human‐driven loss of top predators (Ritchie & Johnson, 2009). However, in many cases, wildlife populations are undergoing alarming declines, and extinction rates are now as high as 100‐fold greater than the background extinction rate (Ceballos et al., 2015). Ecological monitoring is essential for understanding these population dynamics, and rigorous monitoring facilitates informed management. The effectiveness of management decision‐making is often dependent on the accuracy and timeliness of the relevant ecological data upon which decisions are based, meaning that improvements to data collection methods may herald improved ecological outcomes from management actions

An Autonomous Sailboat for Environment Monitoring

The marine environment is constantly at risk from coastal urbanization. The deterioration of coastal and marine environments is evidenced by the decline of mangroves and the biodiversity of such environments and increasing recurrences of algal and jellyfish blooms. There is a lack of environmental data especially in developing countries such as Malaysia to determine the sustainability and impact of the current development on coastal resources. We developed an autonomous sailboat that utilizes the Internet of things technology to collect and analyze ocean water quality data for local authorities to obtain insights into the sustainable development of coastal resources. The USV is equipped with sensors, microcontrollers, and a wireless communication module based on ZigBee standards to allow sending water quality data to a gateway located at the shore. The data collected by the USV will be processed by a cloud server and visualized through user applications

Using drones to improve wildlife monitoring in a changing climate

This thesis advances knowledge of wildlife monitoring techniques and demonstrates the potential of high-resolution, remotely sensed data to inform species conservation, improve ecosystem management and assess mitigation strategies for biodiversity loss. Drones can easily collect systematic, high spatial and temporal resolution data to detect fluctuations in key parameters such as abundance, range and condition of some species. Advances in drone-facilitated wildlife monitoring of sentinel species will provide rapid, efficient insights into ecosystem-level changes. This thesis focused on resolving knowledge gaps within three key areas of wildlife drone-ecology: disturbance, population monitoring and body condition. From the outset, we recognised drones might have undesirable or unforeseen behavioural and physiological effects on wildlife. To address this, I led a time-critical publication that advocated researchers adopt a precautionary approach given the limited understanding of the impacts. It also provided recommendations for conducting drone-facilitated research around wildlife as the basis for a code of best practice. Then, using colonial birds as a study group, we tested the utility of drone-derived data for population monitoring. First, life-sized, replica seabird colonies containing a known number of fake birds were used to robustly assess the accuracy of our intended approach compared to the traditional ground-based counting method. Drone-derived abundance data were, on average, between 43% and 96% more accurate, as well as more precise, than estimates from the traditional approach. Our open-source, semi-automated detection algorithm estimated abundance 94% similar to manual counts from the remotely sensed imagery. To apply this in the field, we collected drone-derived abundance data by repeatedly surveying representative, wild colonial birds (a tern, cormorant and pelican species). We used these data to develop a transferable technique requiring minimal user-input for adaptable and high spatiotemporal population monitoring. Finally, to investigate the use of drone-facilitated photogrammetry, we used a representative pinniped species to test if non-invasively acquired, morphometric data could infer body condition. Drone-derived measurements of endangered Australian sea lions (Neophoca cinerea) of known size and mass were precise and without bias. These two- and three-dimensional measurements from orthomosaics and digital elevation models were highly correlated with animal mass and body condition indices and not significantly different to those generated from ground-collected data. This work addresses and informs a range of issues arising from human activity in the Anthropocene, including rapid habitat loss, species extinctions and an altered climate. We have shown that using technology for wildlife monitoring enables timely, proactive environmental and conservation management.Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 202

屋外調査用自律移動型ロボットの不整地移動性能

早大学位記番号:新7829早稲田大

Diseño y validación del sistema de control para la navegación de un vehículo semiautónomo de superficie /

Los vehículos marinos no tripulados representan una solución cada vez más atractiva a todas las misiones que se desarrollan sobre ambientes acuáticos como inspección, vigilancia, investigación, monitoreo ambiental, desminado, entre otras. Los problemas involucrados en su desarrollo se pueden agrupar bajo tres áreas, las cuales son: guiado, navegación y control. Estas otorgan inteligencia, lectura del exterior más el movimiento y son ampliamente estudiadas con el fin de mejorar las técnicas utilizadas. En este estudio se presenta un repaso del modelamiento de las embarcaciones con el fin de implementar un control que satisfaga las necesidades de movimiento a lo largo de una ruta. También se presenta el filtrado de las señales del sistema de navegación mediante el filtro extendido de kalman (EKF). Se realiza una simulación del sistema y se valida experimentalmente donde se ve que la metodología propuesta es suficiente.Incluye referencia bibliográfic