Autonomous Feature Tracing and Adaptive Sampling in Real-World Underwater Environments

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sensors for underwater environmental sensing. We have also developed a compact high resolution fast temperature sensing module for the AUV for microstructure and turbulence measurements in water bodies. In this paper, we describe a number of algorithms and subsystems of the AUV that enable autonomous real-world operation, and present the data gathered in an experimental campaign in collaboration with limnologists. We demonstrate adaptive sampling missions where the AUV could autonomously locate a zone of interest and adapt its trajectory to stay in it. Further, it could execute specific behaviors to accommodate special sensing requirements necessary to enhance the quality of the data collected. In these missions, the AUV could autonomously trace a feature and capture horizontal variation in various quantities, including turbidity and temperature fluctuations, allowing limnologists to study lake phenomena in an additional dimension

Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience

Hybrid sensor networks (HSN) consist of both static and mobile sensors deployed to fulfill a common monitoring task. The hybrid structure generalizes the network’s design problem and offers a rich set of possibilities for a host of environmental monitoring and anomaly detection applications. HSN also raise a new set of research questions. Their deployment and optimization provide unique opportunities to improve the network’s monitoring performance and resilience. This thesis addresses three challenges associated with HSN related to the collaboration, optimization, and resilience aspects of the network. Broadly speaking, these challenges revolve around the following questions: (1) how to collaboratively allocate the static sensors and devise the path planning of the mobile sensors to improve the monitoring performance? (2) how to select and optimize the sensor portfolio (the mix of each type of sensors) under given cost constraints? And (3) how to embed resilience in a HSN to sustain the monitoring performance in the face of sensor failures and disruptions? In part I, collaboration, this thesis develops a novel deployment strategy for HSN. The strategy solves the static sensor allocation problem, the mobile sensor path planning problem, and most importantly, the collaboration between these two types of sensors. Previous research in this area has addressed these problems separately in simplified environments. In this thesis, a collaborative deployment strategy of HSN is developed to improve the ultimate monitoring performance in complex environments with obstacles and non-uniform risk distribution. In part II, optimization, this thesis addresses the HSN sensor portfolio selection problem. It investigates the tradeoff between the static and mobile sensors to achieve the optimal monitoring performance under different cost constraints. Previous research in this area has studied the optimization problem for networks with a single type of sensor. In this thesis, a general optimization problem is formulated for HSN with static and mobile sensors and solved to identify the optimal portfolio mix and its main drivers. In part III, resilience, this thesis identifies monitoring resilience as a key feature enabled by HSN. This part focuses on the performance degradation of HSN in the presence of sensor failures and disruptions, and it identifies the means to embed resilience in a HSN to mitigate this performance degradation. Monitoring resilience is achieved by accounting for potential sensor failures in the deployment strategy of both static and mobile sensors through a novel, carefully designed probability sum technique. Previous research in this area has examined the reliability problem from a coverage point of view. This thesis extends the scope of investigation of HSN from reliability to resilience, and it shifts the focus from coverage considerations to the actual monitoring performance (e.g., detection time lag) and its resilience in the face of disruptions. To demonstrate and validate this novel perspective on HSN and the associated technical developments, this thesis focused on two examples of fire detection in a multi-room apartment using temperature sensors and CO leak detection in a 3D space station module with ventilation system. Three metrics are adopted as the ultimate monitoring performance, namely the detection time lag, the anomaly source localization uncertainty, and the state estimation error. A simulation environment based on the advection-conduction heat propagation model is developed for the computational experiments. The results (1) demonstrate that the optimal collaborative deployment strategy allocates the static sensors at high-risk locations and directs the mobile sensors to patrol the rest of the low-risk areas; (2) identify a set of conditions under which HSN significantly outperform purely static and purely mobile sensor networks across the three performance metrics here considered; and (3) establish that while sensor failures can considerably degrade the monitoring performance of traditional static sensor networks, the resilient deployment of HSN drastically reduces the performance degradation.Ph.D

Robotic Path Planning for High-Level Tasks in Discrete Environments

This thesis proposes two techniques for solving high-level multi-robot motion planning problems with discrete environments. We focus on an important class of problems that require an allocation of spatially distributed tasks to robots, along with a set of efficient paths for the robots to visit their task locations. The first technique, SAT-TSP, models the problem with a framework that allows a natural coupling between the allocation problem and the path planning problem. The allocation problem is encoded as a Boolean Satisfiability problem (SAT) and the path planning problem is encoded as a Travelling Salesman Problem (TSP). In addition, this framework can handle complex constraints such as battery life limitations, robot carrying capacities, and robot-task incompatibilities. We propose an algorithm that leverages recent advances in Satisfiability Modulo Theory to combine state-of-the-art SAT and TSP solvers. We characterize the correctness of our algorithm and evaluate it in simulation on a series of patrolling, periodic routing, and multi-robot sample collection problems. The results show that our algorithm outperforms a state-of-the-art mathematical programming solver on a majority of the problems in our benchmark, especially the more difficult problems. The second technique, Gamma-Clustering, is used to reduce the computational effort of finding good solutions for metric discrete path planning problems. This technique can be used on the set of allocation path planning problems that do not have ordering constraints (ordering only affects the cost of the solution, not its feasibility). To obtain the computational savings, we find Gamma-Clusters within the problem's environment and then restrict how feasible paths visit these clusters. We prove that solutions found using this approach are within a constant factor of the optimal. By increasing the parameter Gamma we can improve the quality of the bound but we do so with less computational savings. We provide a simple polynomial-time algorithm for finding the optimal Gamma-Clustering and show that for a given Gamma the clustering is unique. We provide two methods for using Gamma-Clusters on path planning problems, a coupled method and a hierarchical method. We demonstrate the effectiveness of these methods on travelling salesman instances, sample collection problems, and period routing problems. The results show that for many instances we obtain significant reductions in computation time with little to no reduction in solution quality. Comparing these methods to a standard integer programming approach reveals that as the problems become more difficult, the solution quality of the two methods degrade at a slower rate than the standard approach, thus for more difficult instances we can use Gamma-Clustering to find higher quality solutions