Induction of Topological Environment Maps from Sequences of Visited Places

In this paper we address the problem of topologically mapping environments which contain inherent perceptual aliasing caused by repeated environment structures. We propose an approach that does not use motion or odometric information but only a sequence of deterministic measurements observed by traversing an environment. Our algorithm implements a stochastic local search to build a small map which is consistent with local adjacency information extracted from a sequence of observations. Moreover, local adjacency information is incorporated to disambiguate places which are physically different but appear identical to the robots senses. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that it infers a small map quickly

Exploring Topological Environments

Simultaneous localization and mapping (SLAM) addresses the task of incrementally building a map of the environment with a robot while simultaneously localizing the robot relative to that map. SLAM is generally regarded as one of the most important problems in the pursuit of building truly autonomous mobile robots. This thesis considers the SLAM problem within a topological framework, in which the world and its representation are modelled as a graph. A topological framework provides a useful model within which to explore fundamental limits to exploration and mapping. Given a topological world, it is not, in general, possible to map the world deterministically without resorting to some type of marking aids. Early work demonstrated that a single movable marker was sufficient but is this necessary? This thesis shows that deterministic mapping is possible if both explicit place and back-link information exist in one vertex. Such 'directional lighthouse' information can be established in a number of ways including through the addition of a simple directional immovable marker to the environment. This thesis also explores non-deterministic approaches that map the world with less marking information. The algorithms are evaluated through performance analysis and experimental validation. Furthermore, the basic sensing and locomotion assumptions that underlie these algorithms are evaluated using a differential drive robot and an autonomous visual sensor

Exploring Topological Environments

Simultaneous localization and mapping (SLAM) addresses the task of incrementally building a map of the environment with a robot while simultaneously localizing the robot relative to that map. SLAM is generally regarded as one of the most important problems in the pursuit of building truly autonomous mobile robots. This thesis considers the SLAM problem within a topological framework, in which the world and its representation are modelled as a graph. A topological framework provides a useful model within which to explore fundamental limits to exploration and mapping. Given a topological world, it is not, in general, possible to map the world deterministically without resorting to some type of marking aids. Early work demonstrated that a single movable marker was sufficient but is this necessary? This thesis shows that deterministic mapping is possible if both explicit place and back-link information exist in one vertex. Such 'directional lighthouse' information can be established in a number of ways including through the addition of a simple directional immovable marker to the environment. This thesis also explores non-deterministic approaches that map the world with less marking information. The algorithms are evaluated through performance analysis and experimental validation. Furthermore, the basic sensing and locomotion assumptions that underlie these algorithms are evaluated using a differential drive robot and an autonomous visual sensor

Robot introspection through learned hidden Markov models

In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability. We assume that the behaviour of a robot in achieving a task can be modelled as a finite stochastic state transition system. Beginning with data recorded by a robot in the execution of a task, we use unsupervised learning techniques to estimate a hidden Markov model (HMM) that can be used both for predicting and explaining the behaviour of the robot in subsequent executions of the task. We demonstrate that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task.The learned HMM can be used both for monitoring and controlling the behaviour of the robot. The ultimate purpose of our work is to learn models for the full set of tasks associated with a given problem domain, and to integrate these models with a generative task planner. We want to show that these models can be used successfully in controlling the execution of a plan. However, this paper does not develop the planning and control aspects of our work, focussing instead on the learning methodology and the evaluation of a learned model. The essential property of the models we seek to construct is that the most probable trajectory through a model, given the observations made by the robot, accurately diagnoses, or explains, the behaviour that the robot actually performed when making these observations. In the work reported here we consider a navigation task. We explain the learning process, the experimental setup and the structure of the resulting learned behavioural models. We then evaluate the extent to which explanations proposed by the learned models accord with a human observer's interpretation of the behaviour exhibited by the robot in its execution of the task

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

Decoupling with random quantum circuits

Decoupling has become a central concept in quantum information theory with applications including proving coding theorems, randomness extraction and the study of conditions for reaching thermal equilibrium. However, our understanding of the dynamics that lead to decoupling is limited. In fact, the only families of transformations that are known to lead to decoupling are (approximate) unitary two-designs, i.e., measures over the unitary group which behave like the Haar measure as far as the first two moments are concerned. Such families include for example random quantum circuits with O(n^2) gates, where n is the number of qubits in the system under consideration. In fact, all known constructions of decoupling circuits use \Omega(n^2) gates. Here, we prove that random quantum circuits with O(n log^2 n) gates satisfy an essentially optimal decoupling theorem. In addition, these circuits can be implemented in depth O(log^3 n). This proves that decoupling can happen in a time that scales polylogarithmically in the number of particles in the system, provided all the particles are allowed to interact. Our proof does not proceed by showing that such circuits are approximate two-designs in the usual sense, but rather we directly analyze the decoupling property.Comment: 25 page

Recommendation & mobile systems - a state of the art for tourism

Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages