709 research outputs found
Contributions to autonomous robust navigation of mobile robots in industrial applications
151 p.Un aspecto en el que las plataformas móviles actuales se quedan atrás en comparación con el punto que se ha alcanzado ya en la industria es la precisión. La cuarta revolución industrial trajo consigo la implantación de maquinaria en la mayor parte de procesos industriales, y una fortaleza de estos es su repetitividad. Los robots móviles autónomos, que son los que ofrecen una mayor flexibilidad, carecen de esta capacidad, principalmente debido al ruido inherente a las lecturas ofrecidas por los sensores y al dinamismo existente en la mayoría de entornos. Por este motivo, gran parte de este trabajo se centra en cuantificar el error cometido por los principales métodos de mapeado y localización de robots móviles,ofreciendo distintas alternativas para la mejora del posicionamiento.Asimismo, las principales fuentes de información con las que los robots móviles son capaces de realizarlas funciones descritas son los sensores exteroceptivos, los cuales miden el entorno y no tanto el estado del propio robot. Por esta misma razón, algunos métodos son muy dependientes del escenario en el que se han desarrollado, y no obtienen los mismos resultados cuando este varía. La mayoría de plataformas móviles generan un mapa que representa el entorno que les rodea, y fundamentan en este muchos de sus cálculos para realizar acciones como navegar. Dicha generación es un proceso que requiere de intervención humana en la mayoría de casos y que tiene una gran repercusión en el posterior funcionamiento del robot. En la última parte del presente trabajo, se propone un método que pretende optimizar este paso para así generar un modelo más rico del entorno sin requerir de tiempo adicional para ello
Automatic Sign Reading and Localization for Semantic Mapping with an Office Robot
Semantic mapping is the task of providing a robot with a map of its
environment beyond the open, navigable space of traditional Simultaneous
Localization and Mapping (SLAM) algorithms by attaching semantics to locations.
The system presented in this work reads door placards to annotate the locations
of offices. Whereas prior work on this system developed hand-crafted detectors,
this system leverages YOLOv5 for sign detection and EAST for text recognition.
Placards are localized by computing their pose from a point cloud in a RGB-D
camera frame localized by a modified ORB-SLAM. Semantic mapping is accomplished
in a post-processing step after robot exploration from video recording. System
performance is reported in terms of the number of placards identified, the
accuracy of their placement onto a SLAM map, the accuracy of the map built, and
the correctness transcribed placard text
GRASP News Volume 9, Number 1
A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory
RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios
Labels are widely used in augmented reality (AR) to display digital
information. Ensuring the readability of AR labels requires placing them
occlusion-free while keeping visual linkings legible, especially when multiple
labels exist in the scene. Although existing optimization-based methods, such
as force-based methods, are effective in managing AR labels in static
scenarios, they often struggle in dynamic scenarios with constantly moving
objects. This is due to their focus on generating layouts optimal for the
current moment, neglecting future moments and leading to sub-optimal or
unstable layouts over time. In this work, we present RL-LABEL, a deep
reinforcement learning-based method for managing the placement of AR labels in
scenarios involving moving objects. RL-LABEL considers the current and
predicted future states of objects and labels, such as positions and
velocities, as well as the user's viewpoint, to make informed decisions about
label placement. It balances the trade-offs between immediate and long-term
objectives. Our experiments on two real-world datasets show that RL-LABEL
effectively learns the decision-making process for long-term optimization,
outperforming two baselines (i.e., no view management and a force-based method)
by minimizing label occlusions, line intersections, and label movement
distance. Additionally, a user study involving 18 participants indicates that
RL-LABEL excels over the baselines in aiding users to identify, compare, and
summarize data on AR labels within dynamic scenes
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
Policy-Based Planning for Robust Robot Navigation
This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems.
Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments.
DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D
occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction.
Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments.
This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the
scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments.
The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd
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
- …