Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes

Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model's prediction uncertainty to identify critical areas for informative data collection. Gaussian Processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary -- different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple, robust, and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note: text overlap with arXiv:2205.0642

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

Topologic Maps for Robotic Exploration of Underground Flooded Mines

The mapping of confined environments in mobile robotics is traditionally tackled in dense occupancy maps, requiring large amounts of storage. For some use cases, such as the exploration of flooded mines, the use of dense maps in processing slow down processes like path generation. I introduce a method of generating topological maps in constrained spaces such as mines. By taking a structure with fewer points, traversal and storage of explored space can be made more efficient, avoiding com plex graphs generated by methods like RRT and it’s variants. It’s simpler structure also allows for more intuitive human-machine interactions with it’s fewer points. I also introduce an autonomous frontier-based exploration approach to generate the topological map during exploration, taking advantage of it’s traversal to navigate through known space. With this work, simulation tests show it is possible to success fully extract a simpler graph structure describing the topology during autonomous exploration and that this structure is robust through explored regionsO mapeamento de ambientes confinados em robótica móvel, é tradicionalmente abordado em mapas densos de ocupação, necessitando de grandes quantidades de armazenamento. Para certos casos, tal como a exploração de minas submersas, o uso de mapas densos no processamento, atrasa processos como geração de caminhos. Utilizando uma estrutura com menos pontos, a travessia e o armazenamento de espaço explorado tornam-se mais eficientes, evitando grafos complexos gerados por métodos como RRT e variantes. A sua estrutura mais simples permite também interações homem-máquina com o seu número reduzido de pontos. Introduzo também uma abordagem autónoma de exploração baseada em fronteiras, para gerar o mapa topo lógico durante a exploração, tirando vantagem da travessia do mesmo para navegar por espaço conhecido. Com este trabalho, testes em simulação mostram ser possível extrair uma estrutura sob forma de grafo, descrevendo a topologia ao longo de explorações autónomas e que esta estrutura é robusta para a travessia em regiões explorada