Browsing by Subject "Path Planning"

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    Active Target Localization and Tracking with Application to Robotic Environmental Monitoring
    (2015-09) Vander Hook, Joshua
    Thanks to advances in miniaturization, computing power, reliable sensors, and battery life, mobile robots are increasingly being used for a wide variety of environmental monitoring tasks. No longer confined to factory floors or controlled environments, robots for remote sensing in dangerous or hard-to-reach environments could provide the same scalability, precision, and reliability to environmental monitoring as they did to industrial applications. To enable this kind of long-term, reliable, autonomous mobile sensor deployment, algorithms which can ensure that the robots achieve their sensing tasks are required. In the first part of the thesis, we study the problem of using one or more mobile robots equipped with bearing sensors to locate a stationary target in minimum time. The problem requires optimizing the measurement locations of the robots to gather the required information about the target's location. In addition, when multiple robots collaborate, we include communication constraints in the path planning objective. Two formulations for this problem are studied. First, we study the offline problem of finding measurement trajectories when the true target location is known. Second, we study the online version and show how to adapt the offline solution to the situation when the target location is not known, while preserving the quality guarantees of the offline solution. In the second part of the thesis, we study the problem of locating multiple stationary targets using a single mobile robot. We formulate a novel coverage problem and provide two main results. We first study the problem of initializing consistent estimate of the targets' locations. These initial estimates are used to seed an active localization algorithm which is shown to localize the targets quickly. In a second formulation, we assume that the targets are within a set of polygonal regions, but have no further information about the distribution or number of targets in the environment. An algorithm is provided which can choose measurement locations to localize all the targets to within desired precision in near optimal time. In the third part of the thesis, we study the problem of using bearing information to track and capture a moving target. We present two formulations based on pursuit-evasion games. In the open plane, the objective is for a mobile robot to minimize the distance to a maneuvering target when only uncertain bearing information is available to the robot. Then, we study the problem of capturing the maneuvering target in a closed environment by moving close to it. We show that the size of the environment relative to the sensing noise determines if this is possible. HASH(0x7febe3ca4040) In addition to theoretical results, we present field studies of using one or more mobile robots to detect radio transmitters using these results. We show that the algorithms presented are suitable for use in monitoring invasive fish.
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    Energy-Aware Robotics For Environmental Monitoring
    (2020-04) Plonski, Patrick
    Outdoor autonomous robots carrying sensors have the potential to provide transformative data for science, industry, and safety. However, limited battery life is a factor that restricts their wide deployment. To fully realize the potential of these robots, this restriction must be loosened. We improve battery life by designing algorithms that allow outdoor sensors to intelligently plan their movements to minimize power consumption while taking the necessary measurements for their tasks. This dissertation focuses on three main problems: The first problem we address is the problem of planning energy-minimal trajectories for solar-powered robots in a cluttered environment that contains shadows. We make contributions to this problem in both planning and estimation. We present a novel planning algorithm that efficiently computes an optimal trajectory on a discretized grid, given a solar map and a target position. We also present three methods of estimating the solar map that relates position and time with the amount of solar energy that can be collected if the robot is in that position at that time, using only previous measurements of solar power tagged with positions and times. The first method uses Gaussian Process regression to directly predict the solar power at a position based on neighboring measurements. The second method uses spatiotemporal Gaussian Process classification to estimate the likelihood that a particular position will be in the sun. The third method uses the measurements of sun and shade to infer shadow-casting objects, and performs raytracing through the uncertain height map to estimate the likely solar map for any given sun angle. These methods are justified through field experiments and in simulation. The second problem we address is the problem of exploring unknown environments in a manner that guarantees bounded motion cost. We consider two variants of this problem. First we consider a boat, equipped with a sonar sensor, that encounters an obstacle while it is attempting to reach a destination. Second we consider a solar-powered vehicle attempting to map the shadows in an environment. For both variants, we present online algorithms and examine their performance using competitive analysis. In competitive analysis, the performance of an online algorithm is compared against the optimal offline algorithm. For obstacle avoidance, the offline algorithm knows the shape of the obstacle. For solar exploration, the offline algorithm knows the geometry of the shadow casting objects. We obtain an O(1) competitive ratio for obstacle avoidance, and an O(log n) competitive ratio for solar exploration, where n is the number of critical points to observe. The strategies for obstacle avoidance are validated through extensive field experiments, and the strategies for exploration are validated with simulations. The third problem we address is a novel coverage problem that arises in aerial surveying applications. The goal is to compute a shortest path that visits a given set of cones--this is the path that maximizes the coverage ability for a UAV with a limited battery size. The apex of each cone is restricted to lie on the ground plane. The common angle alpha of the cones represent the field of view of the onboard camera. The cone heights, which can be varying, correspond with the desired observation quality (e.g. resolution). This problem is a novel variant of the Traveling Salesman Problem with Neighborhoods (TSPN), and we call it Cone-TSPN. Our main contribution is a polynomial time approximation algorithm for Cone-TPSN. We analyze its theoretical performance and show that it returns a solution whose length is at most O(1 + log(hmax/hmin)) times the length of the optimal solution where hmax and hmin are the heights of the tallest and shortest input cones, respectively. We demonstrate the use of our algorithm in a representative precision agriculture application. We further study its performance in simulation using randomly generated cone sets. Our results indicate that the performance of our algorithm is superior to standard solutions. The results in this dissertation have advanced the state of the art in planning energy-minimizing trajectories for outdoor vehicles, by presenting algorithms with strong theoretical guarantees, justified in field experiments and simulations.