Autonomous exploration system: Techniques for interpretation of multispectral data

An on-board autonomous exploration system that fuses data from multiple sensors, and makes decisions based on scientific goals is being developed using a series of artificial neural networks. Emphasis is placed on classifying minerals into broad geological categories by analyzing multispectral data from an imaging spectrometer. Artificial neural network architectures are being investigated for pattern matching and feature detection, information extraction, and decision making. As a first step, a stereogrammetry net extracts distance data from two gray scale stereo images. For each distance plane, the output is the probable mineral composition of the region, and a list of spectral features such as peaks, valleys, or plateaus, showing the characteristics of energy absorption and reflection. The classifier net is constructed using a grandmother cell architecture: an input layer of spectral data, an intermediate processor, and an output value. The feature detector is a three-layer feed-forward network that was developed to map input spectra to four geological classes, and will later be expanded to encompass more classes. Results from the classifier and feature detector nets will help to determine the relative importance of the region being examined with regard to current scientific goals of the system. This information is fed into a decision making neural net along with data from other sensors to decide on a plan of activity. A plan may be to examine the region at higher resolution, move closer, employ other sensors, or record an image and transmit it back to Earth

Autonomous Exploration over Continuous Domains

Motion planning is an essential aspect of robot autonomy, and as such it has been studied for decades, producing a wide range of planning methodologies. Path planners are generally categorised as either trajectory optimisers or sampling-based planners. The latter is the predominant planning paradigm as it can resolve a path efficiently while explicitly reasoning about path safety. Yet, with a limited budget, the resulting paths are far from optimal. In contrast, state-of-the-art trajectory optimisers explicitly trade-off between path safety and efficiency to produce locally optimal paths. However, these planners cannot incorporate updates from a partially observed model such as an occupancy map and fail in planning around information gaps caused by incomplete sensor coverage. Autonomous exploration adds another twist to path planning. The objective of exploration is to safely and efficiently traverse through an unknown environment in order to map it. The desired output of such a process is a sequence of paths that efficiently and safely minimise the uncertainty of the map. However, optimising over the entire space of trajectories is computationally intractable. Therefore, most exploration algorithms relax the general formulation by optimising a simpler one, for example finding the single next best view, resulting in suboptimal performance. This thesis investigates methodologies for optimal and safe exploration over continuous paths. Contrary to existing exploration algorithms that break exploration into independent sub-problems of finding goal points and planning safe paths to these points, our holistic approach simultaneously optimises the coupled problems of where and how to explore. Thus, offering a shift in paradigm from next best view to next best path. With exploration defined as an optimisation problem over continuous paths, this thesis explores two different optimisation paradigms; Bayesian and functional

Autonomous exploration of hierarchical scene graphs

L'exploració robòtica autònoma és un camp de recerca actiu, on els mètodes de percepció robòtica hi abunden. Els mètodes basats en grafs, en particular, són una manera de representar l'entorn de forma eficient, i ofereixen una base sobre la que raonar a alt nivell per resoldre tasques de l'àmbit de la robòtica. Proposem un sistema per generar grafs jeràrquics d'escena automàticament a partir d'entorns foto-realistes. En aquest treball emprem un mètode de percepció basat en grafs, Hydra, en combinació amb un simulador 3D anomenat Habitat-Sim, per explorar i generar representacions en forma de grafs d'escena 3D dels entorns tridimensionals simulats. Aquest sistema i les dades que n'han derivat ens donen una base sobre la que establim un mètode general per resoldre tasques d'exploració en entorns tridimensionals mitjançant Xarxes Neuronals per a Grafs i Aprenentatge per Reforç.La exploración robótica autónoma es un campo de investigación activo, donde los métodos de percepción robótica abundan. Los métodos basados en grafos, en particular, son una forma de representar el entorno de forma eficiente, y ofrecen una base sobre la que razonar a alto nivel para resolver tareas del ámbito de la robótica. Proponemos un sistema para generar grafos jerárquicos de escena automáticamente a partir de entornos fotorealistas. En este trabajo usamos un método de percepción basado en grafos, Hydra, en combinación con un simulador 3D llamado Habitat-Sim, para explorar y generar representaciones en forma de grafos de escena 3D de los entornos tridimensionales simulados. Este sistema y los datos que han derivado de él nos dan una base sobre la que establecemos un método general para resolver tareas de exploración en entornos tridimensionales mediante Redes Neuronales para Grafos y Aprendizaje por Refuerzo.Robotic autonomous exploration is an active field of research, where robot perception pipelines abound. Graph-based pipelines, in particular, are a way to represent the environment efficiently, and provide grounds for reasoning on a high level to solve robotics tasks. We propose a framework to generate hierarchical scene graphs automatically from photo-realistic environments. In this thesis, a graph perception pipeline, Hydra, is employed in combination with Habitat-Sim, a 3D simulator, to explore and generate 3D scene graph representations from the simulated 3D maps. This framework and data have provided the grounds to establish a general pipeline for solving exploration tasks in 3D environments using Graph Neural Networks and Reinforcement Learning.Outgoin

Robust Collision-free Lightweight Aerial Autonomy for Unknown Area Exploration

Collision-free path planning is an essential requirement for autonomous exploration in unknown environments, especially when operating in confined spaces or near obstacles. This study presents an autonomous exploration technique using a small drone. A local end-point selection method is designed using LiDAR range measurement and then generates the path from the current position to the selected end-point. The generated path shows the consistent collision-free path in real-time by adopting the Euclidean signed distance field-based grid-search method. The simulation results consistently showed the safety, and reliability of the proposed path-planning method. Real-world experiments are conducted in three different mines, demonstrating successful autonomous exploration flight in environments with various structural conditions. The results showed the high capability of the proposed flight autonomy framework for lightweight aerial-robot systems. Besides, our drone performs an autonomous mission during our entry at the Tunnel Circuit competition (Phase 1) of the DARPA Subterranean Challenge.Comment: 8 page