An AS framework is developed for combining measurements arriving from mobile robotic sensors of different scales, rates and accuracies, in order to reconstruct a parametric spatio-temporal field. This novel AS algorithm responds to real time measurements by continuously directing robots to locations most likely to yield maximum information about the sensed field. In addition, the localization uncertainty of the robots is minimized by combining the location states and field parameters in a joint Kalman filter formulation. A simultaneous localization algorithm is developed and simulated for localization of a sensor network using geometric constraints of radio connectivity. An adaptive localization algorithm is developed to adaptively navigate a mobile robot such that it optimally minimizes the largest localization uncertainty of a sensor network. Deadlock avoidance techniques developed using the DEC are extended and implemented on a mobile WSN composed of Cybermotion SR2 patrol robots and Berkley motes, such that smooth, deadlock-free resource scheduling occurs in the presence of shared resources. Further, a general mathematical formulation is devel oped for deadlock avoidance in systems with flexible routing, where both shared and routing resources exist. Simulations are done to validate deadlock-free operation.

The test bed is composed of multiple MWSN that enable sensing the environment at various locations. An inexpensive overhead camera serves as an indoor GPS offering infrequent location updates to the mobile sensors. These updates can be fused with the robot's internal location estimates computed via various Kalman filter formulations. A parametric colour field on the ground serves as the environmental field to be estimated. This could be either static as a printed colour field or dynamic as a time-varying field projected from an overhead projector. These dynamic fields could be used to simulate the spread of a forest fire, for instance. Finally, a base station serves as a centralized controller that communicates with all the sensors, captures field samples from various locations and builds an estimate of the field.The following sections provide additional details on the various components of the test bed.

This chapter discusses background work related to the deployment of mobile robots for sampling. Section 3.1 presents different sampling strategies. Section 3.2 describes the density estimation by sampling and sensor fusion. Sections 3.3 and 3.4 explain existing approaches for sampling using static and mobile sensor nodes, respectively. Parametric and non-parametric solutions to the sampling problem are also discussed. Section 3.5 presents the existing approaches for reducing the localization error in mobile robots while sampling. Following this, the chapter focuses on the mathematical formulation of adaptive sampling (AS) problem for parameterized fields, including models, uncertainties and sampling criteria. Section 3.6 discusses sampling strategies such as raster scanning, random sampling, AS and greedy AS (GAS) and provides both a qualitative and a quantitative definition for the AS problem. Section 3.7 presents the extended Kalman filter (EKF) formulation of the AS problem with a single mobile sensor node.

This chapter applies the adaptive sampling (AS) methods developed in the previous chapters to the application of forest fire mapping. The spread of forest fires is modelled as a parametric field, and the AS algorithms are made use of to estimate the fire field. Extensive simulations are carried out to validate the proposed algorithms. The chapter is organized as follows: section 4.1 presents the two parametric models used to describe the spread of forest fires, section 4.2 discusses the parameterization of the field by interpreting remote sensing images, section 4.3 presents the formulation for extended Kalman filter (EKF)-based AS algorithm for spatio temporal distributions and the multi-scale algorithm for mapping of forest fires using AS, and section 4.4 discusses potential field-based path planning for robots navigating through the estimated fire field. Finally, section 4.5 presents the simulation results for complex fields including the forest fires.

In summary, in this chapter we propose a distributed federated filter, and a clustering scheme for the distribution of computational and communication load among N robots performing sampling missions. Estimates of communication and computational load on the robots show that a dramatic reduction can be expected compared to a centralized sampling scheme, while a reduced sampling time in excess of N times can be expected. Further simulations and experiments are needed to verify the efficiency and convergence properties of the distributed filter scheme we propose.

In manufacturing systems, resources are usually application specific with slight flexibility of resource assignment to tasks, whereas in mobile sensor networks, the resources are heterogeneous and capable of performing diverse tasks. Hence, we have shared resources where multiple tasks contend for a single shared resource, or multiple resources contend to perform a single task. In the former case, we have shared resources, and in the latter, routing resources. The need then arises to suitably assign, dispatch and schedule resources in such a manner so as to avoid contention, or circular wait (CW) of resources leading to deadlock. This chapter is organized into the following sections. Section 6.1 discusses the matrix-based discrete event controller (DEC), section 6.2 introduces deadlocks and presents the deadlock avoidance policy along with implementation on the wireless sensor network (WSN) test bed, section 6.3 discusses the issues of deadlock avoidance in the presence of routing resources and section 6.4 presents simulation results of deadlock avoidance both with and without routing resources. Experimental results of deadlock avoidance on the Automation & Robotics Research Institute (ARRI) WSN test bed are also presented in this section.

A range-free approach for adaptive localization of unlocalized sensor nodes employing a mobile robot with GPS is detailed. A mobile robot navigates through the sensor deployment area broadcasting its positional estimate and the uncertainty in its estimate. Distributed computationally inexpensive, discrete-time Kalman filters, implemented on each static sensor node, fuse information obtained over time from the robot to decrease the uncertainty in each node's location estimate. On the other hand, because of dead reckoning and other systematic errors, the robot loses positional accuracy over time. Updates from GPS and from the localized sensor nodes serve in improving the localization uncertainty of the robot. A continuous-discrete extended Kalman filter (CD EKF) running on the mobile robot fuses information from multiple distinct sources (GPS, various sensors nodes) for robot navigation. This two-part procedure achieves simultaneous localization of the sensor nodes and the mobile robot. Also presented in this chapter is an adaptive localization strategy to navigate the mobile robot to the area of least localized sensor nodes. This ensures that the robot manoeuvres to an area where the sensor nodes possess the largest uncertainty in location, so that it can maximize the use fulness of its positional information in best localizing the overall network.