Muhammad F. Mysorewala

Deployment algorithms and indoor experimental vehicles for studying mobile wireless sensor networks

Wireless communication has been traditionally used in robotics to transmit sensory and telemetry information between a robot and a base station. Because research in mobile robotics has typically focused on navigation, mapping and sensor fusion, network oriented problems such as communication bandwidth optimization, coverage and fault tolerance are not usually considered in this context. The motivation behind this research is formulating and solving combined robot navigation issues (such as obstacle avoidance, environment mapping and coverage) with sensor network issues (such as congestion control, routing and node energy minimization). In this paper we present several types of algorithms for mobile wireless sensor nodes (MWSN) as well as experimental results with a fleet of mobile robots and sensors in our lab. The algorithms include adaptive sampling (AS) for distributed field estimation, potential fields (PF) for communication bandwidth optimization, and a discrete event controller (DEC) for mission planning

EKF-based Adaptive Sampling with Mobile Robotic Sensor Nodes

The use of robotics in environmental monitoring applications requires distributed sensor systems optimized for effective estimation of relevant models subject to energy and environmental constraints. The mobile robot nodes are agents facilitating the repositioning of sensors in order to estimate a field distribution. This field distribution could be, for instance, water salinity in a lake, or air pollution over an industrial area. Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. This paper addresses an important problem for the robotic deployment of sensor networks, namely adaptive sampling (AS) by selection and repositioning of nodes in order to optimally estimate the parameters of distributed variable field models. The AS problem is posed as a sensor fusion problem within the extended Kalman filter (EKF) framework. We present simulation and experimental results of 2D deployment scenarios using low-cost mobile sensor robots developed in our lab

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.

Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks

Wireless communication has been traditionally used in robotics to transmit sensory and telemetry information between a robot and a base station. Because research in mobile robotics has typically focused on navigation, mapping and sensor fusion, network oriented problems such as communication bandwidth optimization, coverage and fault tolerance are not usually considered in this context. The motivation behind this research is formulating and solving combined robot navigation issues (such as obstacle avoidance, environment mapping and coverage) with sensor network issues (such as congestion control, routing and node energy minimization). In this paper we present several types of algorithms for mobile wireless sensor nodes (MWSN) as well as experimental results with a fleet of mobile robots and sensors in our lab. The algorithms include adaptive sampling (AS) for distributed field estimation, potential fields (PF) for communication bandwidth optimization, and a discrete event controller (DEC) for mission planning.

Adaptive Sampling using Non-linear EKF with Mobile Robotic Wireless Sensor Nodes

The use of robotics in distributed monitoring applications requires mobile wireless sensors that are deployed efficiently. Efficiency can be defined in multiple ways, such as in terms of the amount of energy expenditure, communication bandwidth or information content. A very important aspect of mobile sensor deployment includes sampling algorithms at location most likely to yield useful information about a field variable of interest. In this paper, we use inexpensive mobile robot nodes built in our lab (ARRI-Bots) as wireless sensor deployment agents, and we use them to demonstrate information efficient algorithms (e.g., adaptive sampling). Each mobile robot node is characterized by sensor measurement noise in addition to localization uncertainty. We use the extended Kalman filter (EKF) to derive quantitative information measures for sampling locations most likely to yield optimal information about the sampled field distribution. We present simulation and experimental results using this approach

Adaptive sampling of parametric fields

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.

Case study: application to forest fire mapping

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.

Test beds for theory

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 robots 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.

Multi-scale adaptive sampling for mapping forest fires

Distributed monitoring applications require wireless sensors that are efficiently deployed using robots. This paper proposes to deploy sensor nodes in order to estimate the time-varying spread of wildfires. We propose a distributed multi-scale adaptive sampling strategy based on neural networks, the extended Kalman filter (EKF) and greedy heuristics, named ldquoEKF-NN-GASrdquo. This strategy combines measurements arriving at different times from sensors at different scale lengths, such as ground, air-borne or space-borne observation platforms. We use the EKF covariance matrix to derive quantitative information measures for sampling locations most likely to yield optimal information about the sampled field distribution. Furthermore, we reconstruct the spatio-temporal forest fire spread, based on parameterized radial basis functions (RBF) neural networks. To replicate the complexity involved in actual fire-spread we simulate it using discrete event cellular automata acting as our ldquotruth modelrdquo. Finally, we present experimental results with ground vehicles that navigate over a ldquovirtual firerdquo projected on the lab floor from a ceiling-mounted projector to emulate a sampling mission performed by aerial robots.