Mutual information-based exploration on continuous occupancy maps

© 2015 IEEE. The problem of active perception with an autonomous robot is studied in this paper. It is proposed that the exploratory behavior of the robot be controlled using mutual information (MI) surfaces between the current map and a one-step look ahead measurements. MI surfaces highlight informative areas for exploration. A novel method for computing these surfaces is described. An approach that exploits structural dependencies of the environment and handles sparse sensor measurements to build a continuous model of the environment, that can then be used to generate MI surfaces is also proposed. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian Process and provide frontier boundaries for further exploration. The continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic termination criterion for a desired sensitivity. The results from publicly available datasets confirm an average improvement of the proposed methodology over comparable standard and state-of-the-art exploratory methods available in the literature by more than 20% and 13% in travel distance and map entropy reduction rate, respectively

SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization information. This paper proposes a novel simultaneous exploration and localization (SEAL) approach, which uses Gaussian Processes (GP)-based information fusion for maximum exploration while performing communication graph optimization for relative localization. Both these cross-dependent objectives were integrated through the Rao-Blackwellization technique. Distributed linearized convex hull optimization is used to select the next-best unexplored region for distributed exploration. SEAL outperformed cutting-edge methods on exploration and localization performance in extensive ROS-Gazebo simulations, illustrating the practicality of the approach in real-world applications.Comment: Accepted to IROS 202

DEUX: Active Exploration for Learning Unsupervised Depth Perception

Depth perception models are typically trained on non-interactive datasets with predefined camera trajectories. However, this often introduces systematic biases into the learning process correlated to specific camera paths chosen during data acquisition. In this paper, we investigate the role of how data is collected for learning depth completion, from a robot navigation perspective, by leveraging 3D interactive environments. First, we evaluate four depth completion models trained on data collected using conventional navigation techniques. Our key insight is that existing exploration paradigms do not necessarily provide task-specific data points to achieve competent unsupervised depth completion learning. We then find that data collected with respect to photometric reconstruction has a direct positive influence on model performance. As a result, we develop an active, task-informed, depth uncertainty-based motion planning approach for learning depth completion, which we call DEpth Uncertainty-guided eXploration (DEUX). Training with data collected by our approach improves depth completion by an average greater than 18% across four depth completion models compared to existing exploration methods on the MP3D test set. We show that our approach further improves zero-shot generalization, while offering new insights into integrating robot learning-based depth estimation

Distributed multi-robot exploration under complex constraints

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Mobile robots have emerged as a prime alternative to explore physical processes of interest. This is particularly relevant in situations that have a high risk for humans, like e.g. in search and rescue missions, and for applications in which it is desirable to reduce the required time and manpower to gather information, like e.g. for environmental analysis. In such context, exploration tasks can clearly benefit from multi-robot coordination. In particular, distributed multi-robot coordination strategies offer enormous advantages in terms of both system¿s efficiency and robustness, compared to single-robot systems. However, most state-of-the-art strategies employ discretization of robots¿ state and action spaces. This makes them computationally intractable for robots with complex dynamics, and limits their generality. Moreover, most strategies cannot handle complex inter-robot constraints like e.g. communication constraints. The goal of this thesis is to develop a distributed multi-robot exploration algorithm that tackles the two aforementioned issues. To achieve this goal we first propose a single-robot myopic approach, in which we build to develop a non-myopic informative path planner. In a second step, we extend our non-myopic single-robot algorithm to the multi-robot case. Our proposed algorithms build on the following techniques: (i) Gaussian Processes (GPs) to model the spatial dependencies of a physical process of interest, (ii) sampling-based planners to calculate feasible paths; (iii) information metrics to guide robots towards informative locations; and (iv) distributed constraint optimization techniques for multi-robot coordination. We validated our proposed algorithms in simulations and experiments. Specifically, we carried out the following experiments: mapping of a magnetic field with a ground-based robot, mapping of a terrain profile with two quadcopters equipped with an ultrasound sensor, and exploration of a simulated wind field with three quadcopters. Results demonstrate the effectiveness of our approach to perform exploration tasks under complex constraints.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin