Online estimation of ocean current from sparse GPS data for underwater vehicles

© 2019 IEEE. Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents

Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE(2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna

Visuo-Haptic Grasping of Unknown Objects through Exploration and Learning on Humanoid Robots

Die vorliegende Arbeit befasst sich mit dem Greifen unbekannter Objekte durch humanoide Roboter. Dazu werden visuelle Informationen mit haptischer Exploration kombiniert, um Greifhypothesen zu erzeugen. Basierend auf simulierten Trainingsdaten wird außerdem eine Greifmetrik gelernt, welche die Erfolgswahrscheinlichkeit der Greifhypothesen bewertet und die mit der größten geschätzten Erfolgswahrscheinlichkeit auswählt. Diese wird verwendet, um Objekte mit Hilfe einer reaktiven Kontrollstrategie zu greifen. Die zwei Kernbeiträge der Arbeit sind zum einen die haptische Exploration von unbekannten Objekten und zum anderen das Greifen von unbekannten Objekten mit Hilfe einer neuartigen datengetriebenen Greifmetrik

Enhanced Learning Strategies for Tactile Shape Estimation and Grasp Planning of Unknown Objects

Grasping is one of the key capabilities for a robot operating and interacting with humans in a real environment. The conventional approaches require accurate information on both object shape and robotic system modeling. The performance, therefore, can be easily influenced by any noise sensor data or modeling errors. Moreover, identifying the shape of an unknown object under some vision-denied conditions is still a challenging problem in the robotics eld. To address this issue, this thesis investigates the estimation of unknown object shape using tactile exploration and the task-oriented grasp planning for a novel object using enhanced learning techniques. In order to rapidly estimate the shape of an unknown object, this thesis presents a novel multi- fidelity-based optimal sampling method which attempts to improve the existing shape estimation via tactile exploration. Gaussian process regression is used for implicit surface modeling with sequential sampling strategy. The main objective is to make the process of sample point selection more efficient and systematic such that the unknown shape can be estimated fast and accurately with highly limited sample points (e.g., less than 1% of number of data set for the true shape). Specifically, we propose to select the next best sample point based on two optimization criteria: 1) the mutual information (MI) for uncertainty reduction, and 2) the local curvature for fidelity enhancement. The combination of these two objectives leads to an optimal sampling process that balances between the exploration of the whole shape and the exploitation of the local area where the higher fidelity (or more sampling) is required. Simulation and experimental results successfully demonstrate the advantage of the proposed method in terms of estimation speed and accuracy over the conventional one, which allows us to reconstruct recognizable 3D shapes using only around optimally selected 0.4% of the original data set. With the available object shape, this thesis also introduces a knowledge-based approach to quickly generate a task-oriented grasp for a novel object. A comprehensive training dataset which consists of specific tasks and geometrical and physical knowledge of grasping is built up from physical experiment. To analyze and e fficiently utilize the training data, a multi-step clustering algorithm is developed based on a self-organizing map. A number of representative grasps are then selected from the entire training dataset and used to generate a suitable grasp for a novel object. The number of representative grasps is automatically determined using the proposed auto-growing method. In addition, to improve the accuracy and efficiency of the proposed clustering algorithm, we also develop a novel method to localize the initial centroids while capturing the outliers. The results of simulation illustrate that the proposed initialization method and the auto-growing method outperform some conventional approaches in terms of accuracy and efficiency. Furthermore, the proposed knowledge-based grasp planning is also validated on a real robot. The results demonstrate the effectiveness of this approach to generate task-oriented grasps for novel objects

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice