1,712 research outputs found
Visual based localization for a Legged Robot
P. 708-715This paper presents a visual based localization
mechanism for a legged robot. Our proposal, fundamented
on a probabilistic approach, uses a precompiled topological
map where natural landmarks like doors or ceiling lights
are recognized by the robot using its on-board camera.
Experiments have been conducted using the AIBO Sony
robotic dog showing that it is able to deal with noisy sensors
like vision and to approximate world models representing
indoor of ce environments. The two major contributions of
this work are the use of this technique in legged robots, and
the use of an active camera as the main sensorS
IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT
This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
A Factor Graph Approach to Multi-Camera Extrinsic Calibration on Legged Robots
Legged robots are becoming popular not only in research, but also in
industry, where they can demonstrate their superiority over wheeled machines in
a variety of applications. Either when acting as mobile manipulators or just as
all-terrain ground vehicles, these machines need to precisely track the desired
base and end-effector trajectories, perform Simultaneous Localization and
Mapping (SLAM), and move in challenging environments, all while keeping
balance. A crucial aspect for these tasks is that all onboard sensors must be
properly calibrated and synchronized to provide consistent signals for all the
software modules they feed. In this paper, we focus on the problem of
calibrating the relative pose between a set of cameras and the base link of a
quadruped robot. This pose is fundamental to successfully perform sensor
fusion, state estimation, mapping, and any other task requiring visual
feedback. To solve this problem, we propose an approach based on factor graphs
that jointly optimizes the mutual position of the cameras and the robot base
using kinematics and fiducial markers. We also quantitatively compare its
performance with other state-of-the-art methods on the hydraulic quadruped
robot HyQ. The proposed approach is simple, modular, and independent from
external devices other than the fiducial marker.Comment: To appear on "The Third IEEE International Conference on Robotic
Computing (IEEE IRC 2019)
On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration
Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented
Dynamic Motion Modelling for Legged Robots
An accurate motion model is an important component in modern-day robotic
systems, but building such a model for a complex system often requires an
appreciable amount of manual effort. In this paper we present a motion model
representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the
need to manually design the form of a motion model, and provides a direct means
of incorporating auxiliary sensory data into the model. This representation and
its accompanying algorithms are validated experimentally using an 8-legged
kinematically complex robot, as well as a standard benchmark dataset. The
presented method not only learns the robot's motion model, but also improves
the model's accuracy by incorporating information about the terrain surrounding
the robot
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