Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. In this article, we develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum and show how the controller can be extended to work with hybrid robot and sensor network systems. We implement the controller on an underwater sensor network with depth adjustment capabilities. Through simulations and in-situ experiments, we verify the functionality and performance of the system and algorithm.
The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings which are organized in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 14th ISER held on June 15-18, 2014 in Marrakech and Essaouira, Morocco. This present fourteenth edition of Experimental Robotics edited by M. Ani Hsieh, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-ce ntered robotics.
Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex large-scale ecosystems. We introduce a method of underwater monitoring using semi-mobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dimension while the sensor nodes are anchored to the bottom of the water column and can move only up and down along the depth of the water column. We develop three different algorithms to optimize the path of the underwater robot and the positions of the sensors to improve the overall quality of sensing of an area of water. The algorithms fall into three categories based on knowledge of the environment: global knowledge, local knowledge, and a decentralized approach. The first algorithm,VoronoiPath, is a global path planning algorithm that uses the concept of Voronoi Tessellation. The second algorithm, TanBugPath, is a local path planning algorithm, inspired from the Tangent Bug method for obstacle avoidance. Finally, the third path planning algorithm, AdaptivePath, optimizes the path by balancing the distance covered by the underwater robot and maximizing the sensing efficiency of both the sensor and the robot. It is based on an adaptive decentralized algorithm and plans the path of the underwater robot by assigning robot waypoints along the depth of the water column, and then adapting them alongside the sensor nodes to obtain the path of the robot. It uses a stable gradient-descent based controller which, we show, converges to a local minimum. We verify the algorithms through simulations and experiments. The VoronoiPath algorithm, generally, results in more efficient sensing paths. However, it is difficult to implement in real world as it needs global information and results in longer robot paths. The TanBugPath algorithm, on the other hand, has good sensing and it plans paths which are a usually shorter under varying conditions. However, all the processing takes place on-board the mobile robot, hence, this approach needs a more advanced robot than other algorithms. Finally, in case of the AdaptivePath algorithm, the in-network sensors calculate the path of the mobile robot in a decentralized manner. A major advantage of this approach is that the the positions of the sensors in the water column also get optimized depending on the path of the mobile robot. However, this algorithm can get stuck in a local minima, and is also dependent on the starting positions of the robot waypoints. For each of the algorithms we perform a detailed analysis and comparison. We identify limitations of each, and provide framework for future improvements.
Over 70% of our planet is covered by water. It is widely believed that the underwater world holds ideas and resources that will fuel much of the next generation of science and business. Unfortunately, underwater operations are fraught with difficulty due to the absence of an easy way to collect and monitor data. In this thesis we propose a novel underwater sensor network designed to mitigate the problems of underwater sensing and communication. A key feature of this system is the ability of individual nodes to control their depth in water. This single degree of freedom allows the network to cooperatively optimize placement for communication and data collection while minimizing time and energy use. The sensor network also enables a GPS-like system for localizing underwater robots to aid in data retrieval and sensing. We develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for modeling 3D properties of the water. We prove that the controller converges to a local minimum, and implement the controller on our underwater sensor network, where each node is capable of adjusting its depth. We verify the algorithm through simulations and in-water experiments. Most applications require that we associate a location with the sensed data. We have developed an underwater mobile robot localization algorithm that allows underwater robots to act as mobile sensors in the sensor network by using ranging information. The algorithm is a minimalist, geometric-based algorithm that only relies on knowing an upper bound on the robot speed and known static node locations. We prove that the algorithm finds the optimal location of the robot and analyze the algorithm in simulation and in water with our underwater sensor network.
Autonomous underwater vehicles (AUVs) are emerging as a promising solution to help us explore and understand the ocean. The global market for AUVs is predicted to grow from 638 million dollars in 2020 to 1,638 million dollars by 2025 – a compound annual growth rate of 20.8 percent. To make AUVs suitable for a wider range of application-specific missions, it is necessary to deploy multiple AUVs to cooperatively perform the localization, tracking and formation tasks. However, weak underwater acoustic communication and the model uncertainty of AUVs make achieving this challenging. This book presents cutting-edge results regarding localization, tracking and formation for AUVs, highlighting the latest research on commonly encountered AUV systems. It also showcases several joint localization and tracking solutions for AUVs. Lastly, it discusses future research directions and provides guidance on the design of future localization, tracking and formation schemes for AUVs. Representing a substantial contribution to nonlinear system theory, robotic control theory, and underwater acoustic communication system, this book will appeal to university researchers, scientists, engineers, and graduate students in control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of AUVs. Moreover, the practical localization, tracking and formation schemes presented provide guidance on exploring the ocean. The book is intended for those with an understanding of nonlinear system theory, robotic control theory, and underwater acoustic communication systems.
A classic in underwater robotics. One of the first volumes in the “Springer Tracts in Advanced Robotics” series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
The long term goals of the project are: (1) To establish systems and algorithms for controlled Lagrangian particle tracking that will be used to improve the accuracy of model based prediction of trajectories of controlled underwater vehicles subjected to ocean current. (2) To achieve a mission planning system for robotic underwater sensor networks that are able to perform automatic or semiautomatic adaptation to extreme ocean conditions and platform failure, deployment, and recovery. We develop a set of automation middleware that implement a set of novel algorithms for robotic underwater sensor networks serving applications of ocean sampling and ocean model improvement. We design novel model adjustment, cooperative control, and distributed sensing algorithms that will be implemented through the automation middleware. The technical objectives include the following: 1. To investigate a new data assimilation procedure--the controlled Lagrangian particle tracking (CLPT)--and its ability to provide feedback adjustments on ocean modelling systems. To design a validation and adjustment algorithm for ocean models based on CLPT. 2. To develop an automatic middleware that integrates ocean models, robot models, and vehicle control systems towards more accurate prediction of the controlled trajectories of robots in the ocean. 3. To investigate cooperative filters and their ability to improve data quality collected by robotic underwater sensor networks. 4. To design automatic mission planning algorithms for missions with multiple objectives and multiple resolutions. To design a set of efficient and effective control and navigation algorithms that utilize ocean flow to increase mobility with guaranteed sampling performance. 5. To develop a mission planning and optimization system that automatically generates control laws and mission definitions based on user input about mission goals and constraints.
This book deals with the state of the art in underwater robotics experiments of dynamic control of an underwater vehicle. The author presents experimental results on motion control and fault tolerance to thrusters’ faults with the autonomous vehicle ODIN. This second substantially improved and expanded edition new features are presented dealing with fault-tolerant control and coordinated control of autonomous underwater vehicles.
