Recursive Bayesian Initialization of Localization Based on Ranging and Dead Reckoning

The initialization of the state estimation in a localization scenario based on ranging and dead reckoning is studied. Specifically, we start with a cooperative localization setup and consider the problem of recursively arriving at a uni-modal state estimate with sufficiently low covariance such that covariance based filters can be used to estimate an agent's state subsequently. A number of simplifications/assumptions are made such that the estimation problem can be seen as that of estimating the initial agent state given a deterministic surrounding and dead reckoning. This problem is solved by means of a particle filter and it is described how continual states and covariance estimates are derived from the solution. Finally, simulations are used to illustrate the characteristics of the method and experimental data are briefly presented

Decentralized cooperative trajectory estimation for autonomous underwater vehicles

Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.Natural Sciences and Engineering Research Council of CanadaDefense Research and Development CanadaUnited States. Office of Naval Research (Grant N00014-13-1-0588

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

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 2013Autonomous marine vehicles are increasingly used in clusters for an array of oceanographic tasks. The effectiveness of this collaboration is often limited by communications: throughput, latency, and ease of reconfiguration. This thesis argues that improved communication on intelligent marine robotic agents can be gained from acting on knowledge gained by improved awareness of the physical acoustic link and higher network layers by the AUV’s decision making software. This thesis presents a modular acoustic networking framework, realized through a C++ library called goby-acomms, to provide collaborating underwater vehicles with an efficient short-range single-hop network. goby-acomms is comprised of four components that provide: 1) losslessly compressed encoding of short messages; 2) a set of message queues that dynamically prioritize messages based both on overall importance and time sensitivity; 3) Time Division Multiple Access (TDMA) Medium Access Control (MAC) with automatic discovery; and 4) an abstract acoustic modem driver. Building on this networking framework, two approaches that use the vehicle’s “intelligence” to improve communications are presented. The first is a “non-disruptive” approach which is a novel technique for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. This system was analyzed on experimental data and implemented on a fielded vehicle. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved. The second approach is “disruptive,” as it changes the vehicle’s course to effect an improvement in the communications channel. A hybrid data- and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication.I wish to acknowledge the sponsors of this research for their generous support of my tuition, stipend, and research: the WHOI/MIT Joint Program, the MIT Presidential Fellowship, the Office of Naval Research (ONR) # N00014-08-1-0011, # N00014-08-1-0013, and the ONR PlusNet Program Graduate Fellowship, the Defense Advanced Research Projects Agency (DARPA) (Deep Sea Operations: Applied Physical Sciences (APS) Award # APS 11-15 3352-006, APS 11-15-3352-215 ST 2.6 and 2.7

Models and Algorithms for Ultra-Wideband Localization in Single- and Multi-Robot Systems

Location is a piece of information that empowers almost any type of application. In contrast to the outdoors, where global navigation satellite systems provide geo-spatial positioning, there are still millions of square meters of indoor space that are unaccounted for by location sensing technology. Moreover, predictions show that people’s activities are likely to shift more and more towards urban and indoor environments– the United Nations predict that by 2020, over 80% of the world’s population will live in cities. Meanwhile, indoor localization is a problem that is not simply solved: people, indoor furnishings, walls and building structures—in the eyes of a positioning sensor, these are all obstacles that create a very challenging environment. Many sensory modalities have difficulty in overcoming such harsh conditions when used alone. For this reason, and also because we aim for a portable, miniaturizable, cost-effective solution, with centimeter-level accuracy, we choose to solve the indoor localization problem with a hybrid approach that consists of two complementary components: ultra-wideband localization, and collaborative localization. In pursuit of the final, hybrid product, our research leads us to ask what benefits collaborative localization can provide to ultra-wideband localization—and vice versa. The road down this path includes diving into these orthogonal sub-domains of indoor localization to produce two independent localization solutions, before finally combining them to conclude our work. As for all systems that can be quantitatively examined, we recognize that the quality of our final product is defined by the rigor of our evaluation process. Thus, a core element of our work is the experimental setup, which we design in a modular fashion, and which we complexify incrementally according to the various stages of our studies. With the goal of implementing an evaluation system that is systematic, repeatable, and controllable, our approach is centered around the mobile robot. We harness this platform to emulate mobile targets, and track it in real-time with a highly reliable ground truth positioning system. Furthermore, we take advantage of the miniature size of our mobile platform, and include multiple entities to form a multi-robot system. This augmented setup then allows us to use the same experimental rigor to evaluate our collaborative localization strategies. Finally, we exploit the consistency of our experiments to perform cross-comparisons of the various results throughout the presented work. Ultra-wideband counts among the most interesting technologies for absolute indoor localization known to date. Owing to its fine delay resolution and its ability to penetrate through various materials, ultra-wideband provides a potentially high ranging accuracy, even in cluttered, non-line-of-sight environments. However, despite its desirable traits, the resolution of non-line-of-sight signals remains a hard problem. In other words, if a non-line-of-sight signal is not recognized as such, it leads to significant errors in the position estimate. Our work improves upon state-of-the-art by addressing the peculiarities of ultra-wideband signal propagation with models that capture the spatiality as well as the multimodal nature of the error statistics. Simultaneously, we take care to develop an underlying error model that is compact and that can be calibrated by means of efficient algorithms. In order to facilitate the usage of our multimodal error model, we use a localization algorithm that is based on particle filters. Our collaborative localization strategy distinguishes itself from prior work by emphasizing cost-efficiency, full decentralization, and scalability. The localization method is based on relative positioning and uses two quantities: relative range and relative bearing. We develop a relative robot detection model that integrates these measurements, and is embedded in our particle filter based localization framework. In addition to the robot detection model, we consider an algorithmic component, namely a reciprocal particle sampling routine, which is designed to facilitate the convergence of a robot’s position estimate. Finally, in order to reduce the complexity of our collaborative localization algorithm, and in order to reduce the amount of positioning data to be communicated between the robots, we develop a particle clustering method, which is used in conjunction with our robot detection model. The final stage of our research investigates the combined roles of collaborative localization and ultra-wideband localization. Numerous experiments are able to validate our overall localization strategy, and show that the performance can be significantly improved when using two complementary sensory modalities. Since the fusion of ultra-wideband positioning sensors with exteroceptive sensors has hardly been considered so far, our studies present pioneering work in this domain. Several insights indicate that collaboration—even if through noisy sensors—is a useful tool to reduce localization errors. In particular, we show that our collaboration strategy can provide the means to minimize the localization error, given that the collaborative design parameters are optimally tuned. Our final results show median localization errors below 10 cm in cluttered environments