Sensor array signal processing : two decades later

Caption title.Includes bibliographical references (p. 55-65).Supported by Army Research Office. DAAL03-92-G-115 Supported by the Air Force Office of Scientific Research. F49620-92-J-2002 Supported by the National Science Foundation. MIP-9015281 Supported by the ONR. N00014-91-J-1967 Supported by the AFOSR. F49620-93-1-0102Hamid Krim, Mats Viberg

The fusion and integration of virtual sensors

There are numerous sensors from which to choose when designing a mobile robot: ultrasonic, infrared, radar, or laser range finders, video, collision detectors, or beacon based systems such as the Global Positioning System. In order to meet the need for reliability, accuracy, and fault tolerance, mobile robot designers often place multiple sensors on the same platform, or combine sensor data from multiple platforms. The combination of the data from multiple sensors to improve reliability, accuracy, and fault tolerance is termed Sensor Fusion.;The types of robotic sensors are as varied as the properties of the environment that need to be sensed. to reduce the complexity of system software, Roboticists have found it highly desirable to adopt a common interface between each type of sensor and the system responsible for fusing the information. The process of abstracting the essential properties of a sensor is called Sensor Virtualization.;Sensor virtualization to date has focused on abstracting the properties shared by sensors of the same type. The approach taken by T. Henderson is simply to expose to the fusion system only the data from the sensor, along with a textual label describing the sensor. We extend Henderson\u27s work in the following manner. First, we encapsulate both the fusion algorithm and the interface layer in the virtual sensor. This allows us to build multi-tiered virtual sensor hierarchies. Secondly, we show how common fusion algorithms can be encapsulated in the virtual sensor, facilitating the integration and replacement of both physical and virtual sensors. Finally, we provide a physical proof of concept using monostatic sonars, vector sonars, and a laser range-finder

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior

This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, several modes of their interferences, and the achievement of triggering conditions in closed-loop robot plans. The main contributions of this article are: (1) PHAMs, a model of concurrent percept-driven behavior, its formalization, and proofs that the model generates probably, qualitatively accurate predictions; and (2) a resource-efficient inference method for PHAMs based on sampling projections from probabilistic action models and state descriptions. We show how PHAMs can be applied to planning the course of action of an autonomous robot office courier based on analytical and experimental results

Sparse Bayesian information filters for localization and mapping

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

Integrated perception, modeling, and control paradigm for bistatic sonar tracking by autonomous underwater vehicles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 357-364).In this thesis, a fully autonomous and persistent bistatic anti-submarine warfare (ASW) surveillance solution is developed using the autonomous underwater vehicles (AUVs). The passive receivers are carried by these AUVs, and are physically separated from the cooperative active sources. These sources are assumed to be transmitting both the frequency-modulated (FM) and continuous wave (CW) sonar pulse signals. The thesis then focuses on providing novel methods for the AUVs/receivers to enhance the bistatic sonar tracking performance. Firstly, the surveillance procedure, called the Automated Perception, is developed to automatically abstract the sensed acoustical data from the passive receiver to the track report that represents the situation awareness. The procedure is executed sequentially by two algorithms: (i) the Sonar Signal Processing algorithm - built with a new dual-waveform fusion of the FM and CW signals to achieve reliable stream of contacts for improved tracking; and (ii) the Target Tracking algorithm - implemented by exploiting information and environmental adaptations to optimize tracking performance. Next, a vehicular control strategy, called the Perception-Driven Control, is devised to move the AUV in reaction to the track report provided by the Automated Perception. The thesis develops a new non-myopic and adaptive control for the vehicle. This is achieved by exploiting the predictive information and environmental rewards to optimize the future tracking performance. The formulation eventually leads to a new information-theoretic and environmental-based control. The main challenge of the surveillance solution then rests upon formulating a model that allows tracking performance to be enhanced via adaptive processing in the Automated Perception, and adaptive mobility by the Perception-Driven Control. A Unified Model is formulated in this thesis that amalgamates two models: (i) the Information-Theoretic Model - developed to define the manner at which the FM and CW acoustical, the navigational, and the environmental measurement uncertainties are propagated to the bistatic measurement uncertainties in the contacts; and (ii) the Environmental-Acoustic Model - built to predict the signal-to-noise power ratios (SNRs) of the FM and CW contacts. Explicit relationships are derived in this thesis using information theory to amalgamate these two models. Finally, an Integrated System is developed onboard each AUV that brings together all the above technologies to enhance the bistatic sonar tracking performance. The system is formulated as a closed-loop control system. This formulation provides a new Integrated Perception, Modeling, and Control Paradigm for an autonomous bistatic ASW surveillance solution using AUVs. The system is validated using the simulated data, and the real data collected from the Generic Littoral Interoperable Network Technology (GLINT) 2009 and 2010 experiments. The experiments were conducted jointly with the NATO Undersea Research Centre (NURC).by Raymond Hon Kit Lum.Sc.D

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Trajectory optimization for target localization using small unmanned aerial vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 189-197).Small unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance missions. One particular mission of interest involves computing location estimates for targets detected by onboard sensors. Combining UAV state estimates with information gathered by the imaging sensors leads to bearing measurements of the target that can be used to determine the target's location. This 3-D bearings-only estimation problem is nonlinear and traditional filtering methods produce biased and uncertain estimates, occasionally leading to filter instabilities. Careful selection of the measurement locations greatly enhances filter performance, motivating the development of UAV trajectories that minimize target location estimation error and improve filter convergence. The objective of this work is to develop guidance algorithms that enable the UAV to fly trajectories that increase the amount of information provided by the measurements and improve overall estimation observability, resulting in proper target tracking and an accurate target location estimate. The performance of the target estimation is dependent upon the positions from which measurements are taken relative to the target and to previous measurements. Past research has provided methods to quantify the information content of a set of measurements using the Fisher Information Matrix (FIM). Forming objective functions based on the FIM and using numerical optimization methods produce UAV trajectories that locally maximize the information content for a given number of measurements. In this project, trajectory optimization leads to the development of UAV flight paths that provide the highest amount of information about the target, while considering sensor restrictions, vehicle dynamics and operation constraints.(cont.) The UAV trajectory optimization is performed for stationary targets, dynamic targets and multiple targets, for many different scenarios of vehicle motion constraints. The resulting trajectories show spiral paths taken by the UAV, which focus on increasing the angular separation between measurements and reducing the relative range to the target, thus maximizing the information provided by each measurement and improving the performance of the estimation. The main drawback of information based trajectory design is the dependence of the Fisher Information Matrix on the true target location. This issue is addressed in this project by executing simultaneous target location estimation and UAV trajectory optimization. Two estimation algorithms, the Extended Kalman Filter and the Particle Filter are considered, and the trajectory optimization is performed using the mean value of the target estimation in lieu of the true target location. The estimation and optimization algorithms run in sequence and are updated in real-time. The results show spiral UAV trajectories that increase filter convergence and overall estimation accuracy, illustrating the importance of information-based trajectory design for target localization using small UAVs.by Sameera S. Ponda.S.M

Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.