1,514 research outputs found
Spatial context-aware person-following for a domestic robot
Domestic robots are in the focus of research in
terms of service providers in households and even as robotic
companion that share the living space with humans. A major
capability of mobile domestic robots that is joint exploration
of space. One challenge to deal with this task is how could we
let the robots move in space in reasonable, socially acceptable
ways so that it will support interaction and communication
as a part of the joint exploration. As a step towards this
challenge, we have developed a context-aware following behav-
ior considering these social aspects and applied these together
with a multi-modal person-tracking method to switch between
three basic following approaches, namely direction-following,
path-following and parallel-following. These are derived from
the observation of human-human following schemes and are
activated depending on the current spatial context (e.g. free
space) and the relative position of the interacting human.
A combination of the elementary behaviors is performed in
real time with our mobile robot in different environments.
First experimental results are provided to demonstrate the
practicability of the proposed approach
Multisensor-based human detection and tracking for mobile service robots
The one of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In the present paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based legs detection using the on-board LRF. The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to be very discriminative also in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera and the information is fused to the legs position using a sequential implementation of Unscented Kalman Filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms.
Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments
Reliable Monte Carlo Localization for Mobile Robots
Reliability is a key factor for realizing safety guarantee of full autonomous
robot systems. In this paper, we focus on reliability in mobile robot
localization. Monte Carlo localization (MCL) is widely used for mobile robot
localization. However, it is still difficult to guarantee its safety because
there are no methods determining reliability for MCL estimate. This paper
presents a novel localization framework that enables robust localization,
reliability estimation, and quick re-localization, simultaneously. The
presented method can be implemented using similar estimation manner to that of
MCL. The method can increase localization robustness to environment changes by
estimating known and unknown obstacles while performing localization; however,
localization failure of course occurs by unanticipated errors. The method also
includes a reliability estimation function that enables us to know whether
localization has failed. Additionally, the method can seamlessly integrate a
global localization method via importance sampling. Consequently, quick
re-localization from failures can be realized while mitigating noisy influence
of global localization. Through three types of experiments, we show that
reliable MCL that performs robust localization, self-failure detection, and
quick failure recovery can be realized
AUV SLAM and experiments using a mechanical scanning forward-looking sonar
Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods
Motion Planning of Intelligent Robots
Robotics is a fast growing industry that is used in everyday life. One of the most popular is intelligent mobile robots that are used for basic conventional use. The purpose of this project is to use the Turtlebot 2 to map and navigate its environment, while avoiding obstacles. Also to incorporate human machine interaction by using gesture control. This report details the research, setup, and programming process of the robot
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