99 research outputs found
Probablistic approaches for intelligent AUV localisation
This thesis studies the problem of intelligent localisation for an autonomous underwater
vehicle (AUV). After an introduction about robot localisation and specific
issues in the underwater domain, the thesis will focus on passive techniques for AUV
localisation, highlighting experimental results and comparison among different techniques.
Then, it will develop active techniques, which require intelligent decisions
about the steps to undertake in order for the AUV to localise itself. The undertaken
methodology consisted in three stages: theoretical analysis of the problem, tests with
a simulation environment, integration in the robot architecture and field trials. The
conclusions highlight applications and scenarios where the developed techniques have
been successfully used or can be potentially used to enhance the results given by current
techniques. The main contribution of this thesis is in the proposal of an active
localisation module, which is able to determine the best set of action to be executed,
in order to maximise the localisation results, in terms of time and efficiency
Behaviour-driven motion synthesis
Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as
well as to hazardous environments, has led to an increased interest in real-world deployment of
robotic agents. Targeted applications require robots to be adept at synthesising complex
motions rapidly across a wide range of tasks and environments. To this end, this thesis
proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We
formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and
integrate them tightly into the motion synthesis process to make real-world deployment in
complex environments practical. We investigate three principal challenges of this proposition.
Firstly, we argue that behavioural samples in form of trajectories are of particular interest to
guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation
that enables the storage and bootstrap of sets of problem-relevant trajectories.
Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task
instantiations that differ significantly from those stored in the framework. We present two
novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online
modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration
based on trajectory segmentation and malleability.
Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We
propose a multi-layer planner that first solves a simplified version of the problem at hand, to
then inform the search for a solution in the constrained space.
Our contributions on efficient motion synthesis via behaviour guidance augment the robots’
capabilities to deal with more complex planning problems, and do so more effectively than
related approaches in the literature by computing better quality paths in lower response time.
We demonstrate our contributions, in both laboratory experiments and field trials, on a
spectrum of planning problems and robotic platforms ranging from high-dimensional
humanoids and robotic arms with a focus on autonomous manipulation in resembling
environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion
synthesis, we have explored the applicability of our findings on disparate robotic fields, such as
grasp and task planning. We have made some of our contributions open-source hoping they
will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science
Alliance (SICSA
Active Object Classification from 3D Range Data with Mobile Robots
This thesis addresses the problem of how to improve the acquisition of 3D range data with a mobile robot for the task of object classification. Establishing the identities of objects in unknown environments is fundamental for robotic systems and helps enable many abilities such as grasping, manipulation, or semantic mapping. Objects are recognised by data obtained from sensor observations, however, data is highly dependent on viewpoint; the variation in position and orientation of the sensor relative to an object can result in large variation in the perception quality. Additionally, cluttered environments present a further challenge because key data may be missing. These issues are not always solved by traditional passive systems where data are collected from a fixed navigation process then fed into a perception pipeline. This thesis considers an active approach to data collection by deciding where is most appropriate to make observations for the perception task. The core contributions of this thesis are a non-myopic planning strategy to collect data efficiently under resource constraints, and supporting viewpoint prediction and evaluation methods for object classification. Our approach to planning uses Monte Carlo methods coupled with a classifier based on non-parametric Bayesian regression. We present a novel anytime and non-myopic planning algorithm, Monte Carlo active perception, that extends Monte Carlo tree search to partially observable environments and the active perception problem. This is combined with a particle-based estimation process and a learned observation likelihood model that uses Gaussian process regression. To support planning, we present 3D point cloud prediction algorithms and utility functions that measure the quality of viewpoints by their discriminatory ability and effectiveness under occlusion. The utility of viewpoints is quantified by information-theoretic metrics, such as mutual information, and an alternative utility function that exploits learned data is developed for special cases. The algorithms in this thesis are demonstrated in a variety of scenarios. We extensively test our online planning and classification methods in simulation as well as with indoor and outdoor datasets. Furthermore, we perform hardware experiments with different mobile platforms equipped with different types of sensors. Most significantly, our hardware experiments with an outdoor robot are to our knowledge the first demonstrations of online active perception in a real outdoor environment. Active perception has broad significance in many applications. This thesis emphasises the advantages of an active approach to object classification and presents its assimilation with a wide range of robotic systems, sensors, and perception algorithms. By demonstration of performance enhancements and diversity, our hope is that the concept of considering perception and planning in an integrated manner will be of benefit in improving current systems that rely on passive data collection
Recommended from our members
Topologically-Guided Robotic Information Gathering
Information gathering tasks, such as terrestrial search and rescue, aerial inspection, and marine monitoring, require robotic unmanned systems to make decisions on how to travel within an environment to maximize or minimize a path-dependent information objective function. The distribution of information throughout the environment is the result of various processes, either natural or human-caused, and so this distribution exhibits an underlying structure. Existing information gathering algorithms seek to implicitly exploit this structure by selecting paths which maximize the robot's time in high-value regions. We see an opportunity to improve the performance of robots in these information gathering tasks by explicitly reasoning over the structure of information, allowing robots to plan their information gathering missions more efficiently and effectively. Topological representations provide an elegant way to describe the structure of an environment using descriptors that are defined relative to a set of features in the environment. Since these descriptors are inherently global, they provide a way for robots to reason directly about their paths within the global context of their operational environments. This additional context enables robotic systems to efficiently plan non-myopically.
To accomplish this goal, this thesis develops four contributions that allow robotic systems to reason about topological structure in field robotics tasks. The first contribution is a method for formalizing topological path constraints using a Mixed Integer Programming formulation to plan. Our second contribution is a system for exploiting expert-provided domain knowledge to track a topological feature using a team of heterogeneous robots. Both of these contributions provide ways to exploit the existence of topological features in the environment to motivate and constrain information gathering tasks. However, these methods require the features to be defined before planning. While methods to identify features exist for well-constructed indoor environments, they do not extend to the less-structured outdoor environments more common in field robotics applications. Our third and fourth contributions address this problem. The third contribution of this thesis is a hierarchical planning algorithm which identifies hotspot regions in an information function and uses them to construct a high-level planning graph, while the fourth is an algorithm for fitting a Topology-Aware Self-Organizing Map to an information function. The benefits of reasoning about the topology of the information field is demonstrated in simulation and field experiments. By incorporating global context about the information gathering task via topology, our methods are able to plan paths that collect more information than a naïve myopic planner. Furthermore, we are able to produce comparable or superior paths more quickly than state-of-the-art planners that do consider the entire path, such as combinatorial branch and bound algorithms
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
On Consistent Mapping in Distributed Environments using Mobile Sensors
The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hybrid SLAM technique can successfully map large complex data sets in an order of magnitude less time than the time taken by the robot to acquire the data. It has low system requirements and has the potential to run on-board a robot to estimate large distributed environments in real time
Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping
This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent
Contributions to Localization, Mapping and Navigation in Mobile Robotics
This thesis focuses on the problem of enabling mobile robots to autonomously build
world models of their environments and to employ them as a reference to self–localization
and navigation.
For mobile robots to become truly autonomous and useful, they must be able of
reliably moving towards the locations required by their tasks. This simple requirement
gives raise to countless problems that have populated research in the mobile robotics
community for the last two decades. Among these issues, two of the most relevant
are: (i) secure autonomous navigation, that is, moving to a target avoiding collisions
and (ii) the employment of an adequate world model for robot self-referencing within
the environment and also for locating places of interest. The present thesis introduces
several contributions to both research fields.
Among the contributions of this thesis we find a novel approach to extend SLAM
to large-scale scenarios by means of a seamless integration of geometric and topological
map building in a probabilistic framework that estimates the hybrid metric-topological
(HMT) state space of the robot path. The proposed framework unifies the research areas
of topological mapping, reasoning on topological maps and metric SLAM, providing
also a natural integration of SLAM and the “robot awakening” problem.
Other contributions of this thesis cover a wide variety of topics, such as optimal
estimation in particle filters, a new probabilistic observation model for laser scanners
based on consensus theory, a novel measure of the uncertainty in grid mapping, an
efficient method for range-only SLAM, a grounded method for partitioning large maps
into submaps, a multi-hypotheses approach to grid map matching, and a mathematical
framework for extending simple obstacle avoidance methods to realistic robots
Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions
Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal.
ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%.
After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine.
Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee.
We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers.
Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true.
We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal
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