Browsing by Author "Bhadauria, Deepak"
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Item Capturing an Evader in a Polygonal Environment With Obstacles(2010-10-11) Bhadauria, Deepak; Gosse, Shaun; Pipp, JosephWe study a pursuit-evasion game in which one or more cops try to capture a robber by moving onto a robber's current location. All players have equal maximum velocities. We show that three cops can capture the robber in any polygonal environment which can contain any finite number of holes.Item Efficient Data Collection from Wireless Nodes under Two-Ring Communication Model(2011-07-29) Bhadauria, Deepak; Tekdas, OnurWe introduce a new geometric routing problem which arises in data muling applications where a mobile robot is charged with collecting data from stationary sensors. The objective is to compute the robot's trajectory and download sequence so as to minimize the time to collect the data from all sensors. The total data collection time has two components: the robot's travel time and the download time. The time to download data from a sensor $s$ is a function of the locations of the robot and $s$: If the robot is a distance $r_{in}$ away from $s$, it can download the sensor's data in $T_{in}$ units of time. If the distance is greater than $r_{in}$ but less than $r_{out}$, the download time is $T_{out} > T_{in}$. Otherwise, the robot can not download the data from $s$. Here, $r_{in}$, $r_{out}$, $T_{in}$ and $T_{out}$ are input parameters. We refer to this model, which is based on recently developed experimental models for sensor network deployments, as the two ring model, and the problem of downloading data from a given set of sensors in minimum amount of time under this model as the Two-Ring Tour (TRT) problem. We present approximation algorithms for the general case which uses solutions to the Traveling Salesperson with Neighborhoods (TSPN) Problem as subroutines. We also present efficient solutions to special but practically important versions of the problem such as uniform and sparse deploymentsItem Path planning algorithms for robots in a data muling system(2010-12) Bhadauria, DeepakWe study two path planning problems that arise in data muling systems where robots are charged to collect data from wireless devices dispersed across a large environment. In such applications, deploying a network of stationary wireless sensors may be infeasible when many relay nodes are deployed to ensure connectivity. Instead, a few robots can be used as data mules to collect data from these devices. The first problem studied in this thesis is to find tours for multiple robots so as to collect data from all sensors in the least amount of time. We refer to this problem as the Data Gathering Problem (DGP). We assume that sensors have a uniform disk communication model. In this model, data can be downloaded from a sensor with fixed rate inside its communication disk. We present an optimal algorithm for the one dimensional version of DGP. For the two dimensional version we present a constant factor approximation algorithm. Afterwards, we present field experiments in which an autonomous robotic data mule collects data from sensor nodes deployed over a large area. Next, we study data collection problem with a more realistic communication model for sensors. In experiments we found that the time taken to download data from a sensor s is a function of the locations of the robot and s: If the robot is a distance rin away from s, it can download the sensor’s data reliably in Tin units of time. If the distance is greater than rin but less than rout , robot can still download data but due to higher packet loss probability the average download time Tout is higher (Tout > Tin). We refer to this model as the Two-Ring communication model and the corresponding path planning problem as the Two-Ring Tour (TRT) problem. We present a constant factor approximation algorithm for the general case. The algorithm uses a polynomial time approximation scheme as a subroutine. Though the scheme has polynomial running time, its running time is impractically large. It is also very complex to implement. Therefore we study special cases of the TRT problem and present efficient algorithms for them. For robotic data mules to be useful, the robots must be capable of operating in the field for extended periods of time. Therefore, in the last part of the thesis we initiate ii the study of solar energy harvesting for robotic navigation. Our primary contribution is an experimental model of energy consumption and harvesting as a function of environmental parameters. We demonstrate the utility of this model in a simple navigation task.