OASIS: Optimal Arrangements for Sensing in SLAM

The number and arrangement of sensors on an autonomous mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of mobile robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we further show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates

Analysis of radiofrequency-based methods for position and velocity determination of autonomous robots in lunar surface exploration missions

The use of distributed systems has been disruptive in almost any industrial sector, from manufacturing to processing plants from environmental monitoring to vehicle control, and many more. It is therefore natural to assess the benefits that such an advantageous engineering paradigm could bring to space exploration. In recent years, we have been witness to the emergence of concepts such as fractionated satellite systems, formation flying, megaconstellations, and femtoswarms. Most of these space missions have evolved from the idea of a decentralization of processes that were formerly performed in platforms conceived as monolithic systems. The application of this concept to robotic systems is not new, and a great deal of scientific contributions on multi-robot systems exists, focusing on different aspects such as cooperative robotics, behavioural or reactive control, distributed artificial intelligence, swarm multi-agent systems etc. The intrinsic advantages of distribution (improved reliability and efficiency, higher robustness, etc.) has been boosted by the exponential growing of computational power density and a simultaneous miniaturization of technology, leading to smaller and more powerful robotic platforms, which could make a distributed robotic system, made of small robotic agents, a powerful substitute to classical large robotic platforms. This thesis proposes, in the framework of multi-robot systems, a localization method for robotic agents in planetary surface exploration scenarios based on RF range and Doppler frequency shift analysis. The relevance of spatial localization awareness in agents belonging to a distributed robotic system is defined in the context of the advantages of robotic exploration. Different range determination techniques and, specifically, the advantages of including Doppler Effect in the determination of the relative position within the robotic system deployed are considered and the strengths and weaknesses analysed accordingly. Special attention is devoted to the noise sources present in the lunar environment, related to a practical (i.e. non-ideal) implementation architecture and its influence on the system performance. From this point of view, we develop a theoretical model for localization accuracy estimation, generated from power spectrum characteristics, in accordance with the system architecture proposed, and consolidated with numerical simulations and a parametrical assessment on a set of real references of components playing a key role in the overall performance. The selected system architecture is then implemented in a representative set-up and tested under laboratory conditions. Algorithms used for carrier frequency generation and frequency measurement are developed, applied and tested in the hardware-on-the-loop breadboard. The results show that Doppler frequency component can be measured with the proposed architecture, yielding a high sensitivity in the determination of relative speed even at standard communication frequencies (UHF), and improving significantly at higher bands (S, C, etc.). This enables the possibility of adding relative speed to relative position determination via sensor fusion techniques, improving the response time and accuracy during navigation through the exploration scenario

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

Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Localizability Optimization for Multi Robot Systems and Applications to Ultra-Wide Band Positioning

RÉSUMÉ: RÉSUMÉ Les Systèmes Multi-Robots (SMR) permettent d’effectuer des missions de manière efficace et robuste du fait de leur redondance. Cependant, les robots étant des véhicules autonomes, ils nécessitent un positionnement précis en temps réel. Les techniques de localisation qui utilisent des Mesures Relatives (MR) entre les robots, pouvant être des distances ou des angles, sont particulièrement adaptées puisqu’elles peuvent bénéficier d’algorithmes coopératifs au sein du SMR afin d’améliorer la précision pour l’ensemble des robots. Dans cette thèse, nous proposons des stratégies pour améliorer la localisabilité des SMR, qui est fonction de deux facteurs. Premièrement, la géométrie du SMR influence fondamentalement la qualité de son positionnement pour des MR bruitées. Deuxièmement, les erreurs de mesures dépendent fortement de la technologie utilisée. Dans nos expériences, nous nous focalisons sur la technologie UWB (Ultra-Wide Band), qui est populaire pour le positionnement des robots en environnement intérieur en raison de son coût modéré et sa haute précision. Par conséquent, une partie de notre travail est consacrée à la correction des erreurs de mesure UWB afin de fournir un système de navigation opérationnel. En particulier, nous proposons une méthode de calibration des biais systématiques et un algorithme d’atténuation des trajets multiples pour les mesures de distance en milieu intérieur. Ensuite, nous proposons des Fonctions de Coût de Localisabilité (FCL) pour caractériser la géométrie du SMR, et sa capacité à se localiser. Pour cela, nous utilisons la Borne Inférieure de Cramér-Rao (BICR) en vue de quantifier les incertitudes de positionnement. Par la suite, nous fournissons des schémas d’optimisation décentralisés pour les FCL sous l’hypothèse de MR gaussiennes ou log-normales. En effet, puisque le SMR peut se déplacer, certains de ses robots peuvent être déployés afin de minimiser la FCL. Cependant, l’optimisation de la localisabilité doit être décentralisée pour être adaptée à des SMRs à grande échelle. Nous proposons également des extensions des FCL à des scénarios où les robots embarquent plusieurs capteurs, où les mesures se dégradent avec la distance, ou encore où des informations préalables sur la localisation des robots sont disponibles, permettant d’utiliser la BICR bayésienne. Ce dernier résultat est appliqué au placement d’ancres statiques connaissant la distribution statistique des MR et au maintien de la localisabilité des robots qui se localisent par filtrage de Kalman. Les contributions théoriques de notre travail ont été validées à la fois par des simulations à grande échelle et des expériences utilisant des SMR terrestres. Ce manuscrit est rédigé par publication, il est constitué de quatre articles évalués par des pairs et d’un chapitre supplémentaire. ABSTRACT: ABSTRACT Multi-Robot Systems (MRS) are increasingly interesting to perform tasks eÿciently and robustly. However, since the robots are autonomous vehicles, they require accurate real-time positioning. Localization techniques that use relative measurements (RMs), i.e., distances or angles, between the robots are particularly suitable because they can take advantage of cooperative schemes within the MRS in order to enhance the precision of its positioning. In this thesis, we propose strategies to improve the localizability of the SMR, which is a function of two factors. First, the geometry of the MRS fundamentally influences the quality of its positioning under noisy RMs. Second, the measurement errors are strongly influenced by the technology chosen to gather the RMs. In our experiments, we focus on the Ultra-Wide Band (UWB) technology, which is popular for indoor robot positioning because of its mod-erate cost and high accuracy. Therefore, one part of our work is dedicated to correcting the UWB measurement errors in order to provide an operable navigation system. In particular, we propose a calibration method for systematic biases and a multi-path mitigation algorithm for indoor distance measurements. Then, we propose Localizability Cost Functions (LCF) to characterize the MRS’s geometry, using the Cramér-Rao Lower Bound (CRLB) as a proxy to quantify the positioning uncertainties. Subsequently, we provide decentralized optimization schemes for the LCF under an assumption of Gaussian or Log-Normal RMs. Indeed, since the MRS can move, some of its robots can be deployed in order to decrease the LCF. However, the optimization of the localizability must be decentralized for large-scale MRS. We also propose extensions of LCFs to scenarios where robots carry multiple sensors, where the RMs deteriorate with distance, and finally, where prior information on the robots’ localization is available, allowing the use of the Bayesian CRLB. The latter result is applied to static anchor placement knowing the statistical distribution of the MRS and localizability maintenance of robots using Kalman filtering. The theoretical contributions of our work have been validated both through large-scale simulations and experiments using ground MRS. This manuscript is written by publication, it contains four peer-reviewed articles and an additional chapter

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