Bhadauria, Deepak2011-05-062011-05-062010-12https://hdl.handle.net/11299/104135University of Minnesota M.S. thesis. December 2010. Major: Computer Science and Engineering. Advisor: Prof. Volkan Isler. 1 computer file (PDF); viii, 66 pages.We 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.en-USComputer Science and EngineeringThesis or Dissertation