7,426 research outputs found
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
A tesselated probabilistic representation for spatial robot perception and navigation
The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
EMBEDDED LEARNING ROBOT WITH FUZZY Q-LEARNING FOR OBSTACLE AVOIDANCE BEHAVIOR
Fuzzy Q-learning is extending of Q-learning algorithm that uses fuzzy inference system to enable Q-learning holding continuous action and state. This learning has been implemented in various robot learning application like obstacle avoidance and target searching. However, most of them have not been realized in embedded robot. This paper presents implementation of fuzzy Q-learning for obstacle avoidance navigation in embedded mobile robot. The experimental result demonstrates that fuzzy Q-learning enables robot to be able to learn the right policy i.e. to avoid obstacle
Navite: A Neural Network System For Sensory-Based Robot Navigation
A neural network system, NAVITE, for incremental trajectory generation and obstacle avoidance is presented. Unlike other approaches, the system is effective in unstructured environments. Multimodal inforrnation from visual and range data is used for obstacle detection and to eliminate uncertainty in the measurements. Optimal paths are computed without explicitly optimizing cost functions, therefore reducing computational expenses. Simulations of a planar mobile robot (including the dynamic characteristics of the plant) in obstacle-free and object avoidance trajectories are presented. The system can be extended to incorporate global map information into the local decision-making process.Defense Advanced Research Projects Agency (AFOSR 90-0083); Office of Naval Research (N00014-92-J-l309); Consejo Nacional de Ciencia y TecnologĂa (63l462
Positional estimation techniques for an autonomous mobile robot
Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented
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