Browsing by Author "Vander Hook, Joshua"

Now showing 1 - 9 of 9
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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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    Bearing-Only Active Target Localization Strategies for a System of Two Communicating Mobile Robots: Full Technical Report
    (2013-04-08) Vander Hook, Joshua; Tokekar, Pratap
    We study the problem of locating a stationary target using two robots equipped with bearing sensors. The goal is to reduce the uncertainty in the target's location to a value below a given threshold in minimum time. Our cost formulation explicitly models time spent in traveling as well as taking measurements. Further, the robots are subject to distance-based communication constraints. We first study properties of an optimal strategy which has access to the target's true location. It turns out that under certain circumstances, the optimal algorithm will break communication to take measurements and rendezvous to merge them. Using these insights, we design an online strategy (which does not have access to the target's true location) and show that the strategy can locate the target up to a desired uncertainty level at near-optimal cost. The results are applicable to other bearing sensors with non-zero measurement cost, such as pan- tilt cameras. In addition to theoretical analysis, we validate the algorithm in simulations and field experiments performed using autonomous surface vehicles. This full version includes proofs which are not included in the conference version.
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    Cautious Greedy Strategy for Bearing-based Active Localization: Technical Report
    (2011-09-16) Vander Hook, Joshua; Tokekar, Pratap
    We study a novel sensing model which occurs in wildlife monitoring applications. The sensor can be approximated as a bearing-to-target sensor, but with several critical attributes that set it apart from those addressed in bearing-only tracking literature. We propose a measurement strategy that can guarantee reduction in uncertainty in competitive time, regardless of the problem instance. We provide theoretical analysis in the form of a competitive proof, numerical studies, and results from field trials which confirm the applicability of the algorithm.
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    Constrained Probabilistic Search for a One-Dimensional Random Walker
    (2015-12-21) Noori, Narges; Renzaglia, Alessandro; Vander Hook, Joshua; Isler, Volkan
    This paper addresses a fundamental search problem in which a searcher subject to time and energy constraints tries to find a mobile target. The target's motion is modeled as a random walk on a discrete set of points on the line: at each time step the target chooses one of the adjacent nodes at random and moves there. We study two detection models. In the no-crossing model, the searcher detects the target if they are on the same node or if they take the same edge at the same time. In the crossing model, detection happens only if they land on the same node at the same time. For the no-crossing model, where move and stay actions may have different costs, we present an optimal search strategy under energy and time constraints. For the crossing model, we formulate the problem of designing an optimal strategy as a Partially Observable Markov Decision Process (POMDP) and solve it using methods which reduce the state space representation of the belief. The POMDP solution reveals structural properties of the optimal solution. We use this structure to design an efficient strategy and analytically study its performance. Finally, we present preliminary experimental results to demonstrate the applicability of our model to our tracking system which is used for finding radio-tagged invasive fish.
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    Environment and Solar Map Construction for Solar-Powered Mobile Systems
    (2015-04-16) Plonski, Patrick A.; Vander Hook, Joshua; Isler, Volkan
    Energy harvesting using solar panels can significantly increase the operational life of mobile robots. If a map of expected solar power is available, energy efficient paths can be computed. However, estimating this map is a challenging task, especially in complex environments. In this paper, we show how the problem of estimating solar power can be decomposed into the steps of magnitude estimation and solar classification. Then we provide two methods to classify a position as sunny or shaded: a simple data-driven Gaussian Process method, and a method which estimates the geometry of the environment as a latent variable. Both of these methods are practical when the training measurements are sparse, such as with a simple robot that can only measure solar power at its own position. We demonstrate our methods on simulated, randomly generated environments. We also justify our methods with measured solar data by comparing the constructed height maps with satellite images of the test environments, and in a cross-validation step where we examine the accuracy of predicted shadows and solar current.
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    Gathering Bearing Data for Target Localization
    (2015-09-28) Bayram, Haluk; Vander Hook, Joshua; Isler, Volkan
    We consider the problem of gathering bearing data in order to localize targets. We start with a commonly used notion of uncertainty based on Geometric Dilution of Precision (GDOP) and study the following bi-criteria problem. Given a set of potential target locations and an uncertainty level U, compute an ordered set of measurement locations for a single robot which (i) minimizes the total cost given by the travel time plus the time spent in taking measurements, and (ii) ensures that the uncertainty in estimating the target’s location is at most U regardless of the targets’ locations. We present an approximation algorithm and prove that its cost is at most 28.9 times the optimal cost while guaranteeing that the uncertainty is at most 5.5U. In addition to theoretical analysis, we validate the results in simulation and experiments performed with a directional antenna used for tracking invasive fish.
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    Navigation Around an Unknown Obstacle for Autonomous Surface Vehicles Using a Forward-Facing Sonar
    (2015-04-16) Plonski, Patrick A.; Vander Hook, Joshua; Peng, Cheng; Noori, Narges; Isler, Volkan
    A robotic boat is moving between two points when it encounters an obstacle of unknown size. The boat must find a short path around the obstacle to resume its original course. How should the boat move when it can only sense the proximity of the obstacle, and does not have prior information about the obstacle’s size? We study this problem for a robotic boat with a forward-facing sonar. We study two versions of the problem. First, we solve a simplified case when the obstacle is a rectangle of known orientation but unknown dimensions. Second, we study a more general case where an arbitrarily shaped obstacle is contained between two known parallel lines. We study the performance of the algorithms analytically using competitive analysis and present results from field experiments. The experimental setup is relevant for harbor patrol or autonomous navigation in shallow water.
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    Pursuit and Evasion with Uncertain Bearing Measurements
    (2014-01-07) Vander Hook, Joshua; Isler, Volkan
    We study pursuit-evasion games in which a pursuer tries to capture an evader by moving on to the evader’s current location. We investigate how sensing capability of the pursuer affects the game outcome. In particular, we consider a pursuer which can sense only the bearing to an evader. Furthermore, there is noise in the measurements in such a way that an adversary may adjust each bearing measured by an angle up to ? away from the true value. In this work, we study two classical pursuit evasion games under bearing uncertainty. In the first game, played on the open plane, the pursuer tries to maintain the distance to an evader with equal speed. If the pursuer has full knowledge of the evaders location the pursuer can maintain the separation between the players by moving toward the evader. However, when an adversarial sensing model is introduced, we show that for any pursuer strategy, the evader can increase the distance to the pursuer indefinitely. The rate at which the distance increases is linear in time. ?. In the second game, both players are inside a bounded circular area. This version is known as the Lion-and-Man game, and has been well studied when no sensing limitations are imposed. In particular, the pursuer (Lion) is known to have an O(rlogr) strategy to capture the evader, where r is the radius of the circle. In contrast, when sensing uncertainty is introduced, we show that for any ? > 0, there exist environments in which the man can evade capture indefinitely.
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    Sensor Planning for a Symbiotic UAV and UGV system for Precision Agriculture
    (2013-03-25) Tokekar, Pratap; Vander Hook, Joshua; Mulla, David; Isler, Volkan
    We study the problem of coordinating an Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) to collect data for a precision agriculture application. In this application, the ground and aerial measurements collected by the system are used for estimating Nitrogen~(N) levels across a field. These estimates in turn guide fertilizer application. The capability to apply the right amount of fertilizer at the right time can drastically reduce fertilizer usage which is desirable from an environmental and economic standpoint. We propose to use a symbiotic UAV and UGV system in which the UGV is capable of muling the UAV to a deployment location and picking it up later. This would allow the system to overcome the short battery life of a typical UAV. Toward building such a system, the paper makes the following contributions: We start with a prior N-distribution (which can be obtained from a satellite image) over the field. The goal is to classify each point into a fixed number of classes indicating N-stress levels. First, we present a method to identify Potentially Mislabeled (PML) points which are points whose probability of being mislabeled is above a threshold. Second, we study the problem of planning the UAV path so that it can visit the maximum number of PML points subject to its energy budget. The novelty of the formulation is the capability of the UGV to mule the UAV to deployment points. Third, we introduce a new path planning problem in which the UGV must take a measurement near each PML point visited by the UAV. The goal is to minimize the total time spent in traveling and taking measurements. For both problems, we present constant-factor approximation algorithms. Finally, we demonstrate the utility of the system and our algorithms with simulations which use manually collected data from the field as well as realistic energy models for the UAV and the UGV.