We explore synergies among mobile robots and wireless sensor networks in environmental monitoring through a system in which robotic data mules collect measurements gathered by sensing nodes. A proof of concept implementation demonstrates that this approach significantly increases the system's lifetime by conserving energy that the sensing nodes would otherwise use for communication.

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We present a robotic system for collecting data from wireless devices dispersed across a large environment. In such applications, deploying a network of stationary wireless sensors may be infeasible because many relay nodes must be deployed to ensure connectivity. Instead, our system utilizes robots which act as data mules and gather the data from wireless sensor network nodes.We address the problem of planning paths of multiple robots so as to collect the data from all sensors in the least amount of time. In this new routing problem, which we call the Data Gathering Problem (DGP), the total download time depends on not only the robots' travel time but also the time to download data from a sensor, and the number of sensors assigned to the robot.We start with a special case of DGP where the robots' motion is restricted to a curve which contains the base station at one end. For this version, we present an optimal algorithm. * A preliminary version of this paper appeared in IROS'09 (Bhadauria and Isler, 2009).Next, we study the 2D version, and present a constant factor approximation algorithm for DGP on the plane. Finally, we present field experiments in which an autonomous robotic data mule collects data from the nodes of a wireless sensor network deployed over a large field.

Mobile robots equipped with wireless networking capabilities can act as robotic routers and provide network connectivity to mobile users. Robotic routers provide cost efficient solutions for deployment of a wireless network in a large environment with a limited number of users.In this paper, we present motion planning algorithms for robotic routers to maintain the connectivity of a single user to a base station. We consider two motion models for the user. In the first model, the user's motion is known in advance. In the second model, the user moves in an adversarial fashion and tries to break the connectivity. We present optimal motion planning strategies for both models. We also present details of a proof-of-concept implementation.

We introduce a new geometric robot 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 grid-based and sparse deployments. The approach is validated in outdoor experiments.