Cartographie hybride pour des environnements de grande taille

In this thesis, a novel vision based hybrid mapping framework which exploits metric, topological and semantic information is presented. We aim to obtain better computational efficiency than pure metrical mapping techniques, better accuracy as well as usability for robot guidance compared to the topological mapping. A crucial step of any mapping system is the loop closure detection which is the ability of knowing if the robot is revisiting a previously mapped area. Therefore, we first propose a hierarchical loop closure detection framework which also constructs the global topological structure of our hybrid map. Using this loop closure detection module, a hybrid mapping framework is proposed in two step. The first step can be understood as a topo-metric map with nodes corresponding to certain regions in the environment. Each node in turn is made up of a set of images acquired in that region. These maps are further augmented with metric information at those nodes which correspond to image sub-sequences acquired while the robot is revisiting the previously mapped area. The second step augments this model by using road semantics. A Conditional Random Field based classification on the metric reconstruction is used to semantically label the local robot path (road in our case) as straight, curved or junctions. Metric information of regions with curved roads and junctions is retained while that of other regions is discarded in the final map. Loop closure is performed only on junctions thereby increasing the efficiency and also accuracy of the map. By incorporating all of these new algorithms, the hybrid framework presented can perform as a robust, scalable SLAM approach, or act as a main part of a navigation tool which could be used on a mobile robot or an autonomous car in outdoor urban environments. Experimental results obtained on public datasets acquired in challenging urban environments are provided to demonstrate our approach.Dans cette thèse, nous présentons une nouvelle méthode de cartographie visuelle hybride qui exploite des informations métriques, topologiques et sémantiques. Notre but est de réduire le coût calculatoire par rapport à des techniques de cartographie purement métriques. Comparé à de la cartographie topologique, nous voulons plus de précision ainsi que la possibilité d’utiliser la carte pour le guidage de robots. Cette méthode hybride de construction de carte comprend deux étapes. La première étape peut être vue comme une carte topo-métrique avec des nœuds correspondants à certaines régions de l’environnement. Ces cartes sont ensuite complétées avec des données métriques aux nœuds correspondant à des sous-séquences d’images acquises quand le robot revenait dans des zones préalablement visitées. La deuxième étape augmente ce modèle en ajoutant des informations sémantiques. Une classification est effectuée sur la base des informations métriques en utilisant des champs de Markov conditionnels (CRF) pour donner un label sémantique à la trajectoire locale du robot (la route dans notre cas) qui peut être "doit", "virage" ou "intersection". L’information métrique des secteurs de route en virage ou en intersection est conservée alors que la métrique des lignes droites est effacée de la carte finale. La fermeture de boucle n’est réalisée que dans les intersections ce qui accroît l’efficacité du calcul et la précision de la carte. En intégrant tous ces nouveaux algorithmes, cette méthode hybride est robuste et peut être étendue à des environnements de grande taille. Elle peut être utilisée pour la navigation d’un robot mobile ou d’un véhicule autonome en environnement urbain. Nous présentons des résultats expérimentaux obtenus sur des jeux de données publics acquis en milieu urbain pour démontrer l’efficacité de l’approche proposée

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Smart Camera Robotic Assistant for Laparoscopic Surgery

The cognitive architecture also includes learning mechanisms to adapt the behavior of the robot to the different ways of working of surgeons, and to improve the robot behavior through experience, in a similar way as a human assistant would do. The theoretical concepts of this dissertation have been validated both through in-vitro experimentation in the labs of medical robotics of the University of Malaga and through in-vivo experimentation with pigs in the IACE Center (Instituto Andaluz de Cirugía Experimental), performed by expert surgeons.In the last decades, laparoscopic surgery has become a daily practice in operating rooms worldwide, which evolution is tending towards less invasive techniques. In this scenario, robotics has found a wide field of application, from slave robotic systems that replicate the movements of the surgeon to autonomous robots able to assist the surgeon in certain maneuvers or to perform autonomous surgical tasks. However, these systems require the direct supervision of the surgeon, and its capacity of making decisions and adapting to dynamic environments is very limited. This PhD dissertation presents the design and implementation of a smart camera robotic assistant to collaborate with the surgeon in a real surgical environment. First, it presents the design of a novel camera robotic assistant able to augment the capacities of current vision systems. This robotic assistant is based on an intra-abdominal camera robot, which is completely inserted into the patient’s abdomen and it can be freely moved along the abdominal cavity by means of magnetic interaction with an external magnet. To provide the camera with the autonomy of motion, the external magnet is coupled to the end effector of a robotic arm, which controls the shift of the camera robot along the abdominal wall. This way, the robotic assistant proposed in this dissertation has six degrees of freedom, which allow providing a wider field of view compared to the traditional vision systems, and also to have different perspectives of the operating area. On the other hand, the intelligence of the system is based on a cognitive architecture specially designed for autonomous collaboration with the surgeon in real surgical environments. The proposed architecture simulates the behavior of a human assistant, with a natural and intuitive human-robot interface for the communication between the robot and the surgeon

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

An incremental clustering and associative learning architecture for intelligent robotics

The ability to learn from the environment and memorise the acquired knowledge is essential for robots to become autonomous and versatile artificial companions. This thesis proposes a novel learning and memory architecture for robots, which performs associative learning and recall of sensory and actuator patterns. The approach avoids the inclusion of task-specific expert knowledge and can deal with any kind of multi-dimensional real-valued data, apart from being tolerant to noise and supporting incremental learning. The proposed architecture integrates two machine learning methods: a topology learning algorithm that performs incremental clustering, and an associative memory model that learns relationship information based on the co-occurrence of inputs. The evaluations of both the topology learning algorithm and the associative memory model involved the memorisation of high-dimensional visual data as well as the association of symbolic data, presented simultaneously and sequentially. Moreover, the document analyses the results of two experiments in which the entire architecture was evaluated regarding its associative and incremental learning capabilities. One experiment comprised an incremental learning task with visual patterns and text labels, which was performed both in a simulated scenario and with a real robot. In a second experiment a robot learned to recognise visual patterns in the form of road signs and associated them with di erent con gurations of its arm joints. The thesis also discusses several learning-related aspects of the architecture and highlights strengths and weaknesses of the proposed approach. The developed architecture and corresponding ndings contribute to the domains of machine learning and intelligent robotics

Robust convex optimisation techniques for autonomous vehicle vision-based navigation

This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods

Methods for the Efficient Deployment and Coordination of Swarm Robotic Systems

Swarming has been observed in many animal species, including fish, birds, insects and mammals. These biological observations have inspired mathematical models of distributed coordination that have been applied to the development of multi-agent robotic systems, such as collections of unmanned autonomous vehicles (UAVs). The advantages of a swarming approach to distributed coordination are clear: each agent acts according to a simple set of rules that can be implemented on resource-constrained devices, and so it becomes feasible to replicate agents in order to build more resilient systems. However, there remain significant challenges in making the approach practicable. This thesis addresses two of the most significant: coordination and scalability. New coordination algorithms are proposed here, all of which manage the problem of scalability by requiring only local proximity sensing between agents, without the need for any other communications infrastructure. A major source of inefficiency in the deployment of a swarm is ‘oscillation’: small movements of agents that arise as a side effect of the application of their rules but which are not strictly necessary in order to satisfy the overall system function. The thesis introduces a new metric for ‘oscillation’ that allows it to be identified and measured in swarm control algorithms. A new perimeter detection mechanism is introduced and applied to the coordination of goal-based swarms. The mechanism is used to improve the internal coordination of agents whilst maintaining a directional focus to the swarm; this is then analysed using the new metric. A mechanism is proposed to allow a swarm to exhibit a ‘healing’ behaviour by identifying internal perimeter edges (doughnuts) and then altering the movement of agents, based upon a simple criterion, to remove the holes; this also has the emergent effect of smoothing the outer edges of a swarm and creating a more uniform swarm structure. Area coverage is an important requirement in many swarm applications. Two new, efficient area-filling techniques are introduced here and exit conditions are identified to determine when a swarm has filled an area. In summary, the thesis makes significant contributions to the analysis and design of efficient control algorithms for the coordination of large scale swarms

Biologically Inspired Topological Gaussian ARAM for Robot Navigation

<p>This paper presents a neural network for online topological map construction inspired by the beta oscillations and hippocampal place cell learning. In our proposed method, nodes in the topological map represent place cells (robot location) while edges connect nodes and store robot action (i.e. orientation, direction). Our proposed method (TGARAM) comprises 2 layers: the input layer and the memory layer. The input layer collects sensory information and cluster the obtained information into a set of topological nodes incrementally. In the memory layer, the clustered information is used as a topological map where nodes are associated with actions. Then, topological nodes are clustered together into space regions to represent the environment in the memory layer. The advantages of the proposed method are that 1) it does not require high-level cognitive processes and prior knowledge which is able to work in natural environment, 2) it can process multiple sensory sources simultaneously in continuous space, and 3) it is an incremental and unsupervised learning method. Thus, topological map generated by TGARAM is utilised for path planning to constitutes a basis for robot navigation. Finally, we validate the proposed method through several experiments.</p