805 research outputs found
ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning
International audienceIn this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches
Towards a Probabilistic Roadmap for Multi-robot Coordination
International audienceIn this paper, we discuss the problem of multi-robot coordination and propose an approach for coordinated multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, called ACS-PRM, is a sampling-based method and consists of three steps including C-space sampling, roadmap building and motion planning. In contrast to previous approaches, our approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the system efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be obviously reduced by our ACS-PRM approach compared with previous approaches
Robotics Middleware: A Comprehensive Literature Survey and Attribute-Based Bibliography
Autonomous robots are complex systems that require the interaction between numerous heterogeneous components (software and hardware). Because of the increase in complexity of robotic applications and the diverse range of hardware, robotic middleware is designed to manage the complexity and heterogeneity of the hardware and applications, promote the integration of new technologies, simplify software design, hide the complexity of low-level communication and the sensor heterogeneity of the sensors, improve software quality, reuse robotic software infrastructure across multiple research efforts, and to reduce production costs. This paper presents a literature survey and attribute-based bibliography of the current state of the art in robotic middleware design. The main aim of the survey is to assist robotic middleware researchers in evaluating the strengths and weaknesses of current approaches and their appropriateness for their applications. Furthermore, we provide a comprehensive set of appropriate bibliographic references that are classified based on middleware attributes.http://dx.doi.org/10.1155/2012/95901
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP
201
Perception-Action Coupling Target Tracking Control for a Snake Robot via Reinforcement Learning
Visual-guided locomotion for snake-like robots is a challenging task, since it involves not only the complex body undulation with many joints, but also a joint pipeline that connects the vision and the locomotion. Meanwhile, it is usually difficult to jointly coordinate these two separate sub-tasks as this requires time-consuming and trial-and-error tuning. In this paper, we introduce a novel approach for solving target tracking tasks for a snake-like robot as a whole using a model-free reinforcement learning (RL) algorithm. This RL-based controller directly maps the visual observations to the joint positions of the snake-like robot in an end-to-end fashion instead of dividing the process into a series of sub-tasks. With a novel customized reward function, our RL controller is trained in a dynamically changing track scenario. The controller is evaluated in four different tracking scenarios and the results show excellent adaptive locomotion ability to the unpredictable behavior of the target. Meanwhile, the results also prove that the RL-based controller outperforms the traditional model-based controller in terms of tracking accuracy
Cooperative Relative Positioning of Mobile Users by Fusing IMU Inertial and UWB Ranging Information
Relative positioning between multiple mobile users is essential for many
applications, such as search and rescue in disaster areas or human social
interaction. Inertial-measurement unit (IMU) is promising to determine the
change of position over short periods of time, but it is very sensitive to
error accumulation over long term run. By equipping the mobile users with
ranging unit, e.g. ultra-wideband (UWB), it is possible to achieve accurate
relative positioning by trilateration-based approaches. As compared to vision
or laser-based sensors, the UWB does not need to be with in line-of-sight and
provides accurate distance estimation. However, UWB does not provide any
bearing information and the communication range is limited, thus UWB alone
cannot determine the user location without any ambiguity. In this paper, we
propose an approach to combine IMU inertial and UWB ranging measurement for
relative positioning between multiple mobile users without the knowledge of the
infrastructure. We incorporate the UWB and the IMU measurement into a
probabilistic-based framework, which allows to cooperatively position a group
of mobile users and recover from positioning failures. We have conducted
extensive experiments to demonstrate the benefits of incorporating IMU inertial
and UWB ranging measurements.Comment: accepted by ICRA 201
An informationally structured room for robotic assistance
The application of assistive technologies for elderly people is one of the most promising and interesting scenarios for intelligent technologies in the present and near future. Moreover, the improvement of the quality of life for the elderly is one of the first priorities in modern countries and societies. In this work, we present an informationally structured room that is aimed at supporting the daily life activities of elderly people. This room integrates different sensor modalities in a natural and non-invasive way inside the environment. The information gathered by the sensors is processed and sent to a centralized management system, which makes it available to a service robot assisting the people. One important restriction of our intelligent room is reducing as much as possible any interference with daily activities. Finally, this paper presents several experiments and situations using our intelligent environment in cooperation with our service robot. © 2015 by the authors
CompaRob: the shopping cart assistance robot
Technology has recently been developed which offers an excellent opportunity to design systems with the ability to help people
in their own houses. In particular, assisting elderly people in their environments is something that can significantly improve their
quality of life. However, helping elderly people outside their usual environment is also necessary, to help them to carry out daily
tasks like shopping. In this paper we present a person-following shopping cart assistance robot, capable of helping elderly people
to carry products in a supermarket. First of all, the paper presents a survey of related systems that perform this task, using different
approaches, such as attachable modules and computer vision. After that, the paper describes in detail the proposed system and its
main features. The cart uses ultrasonic sensors and radio signals to provide a simple and effective person localization and following
method. Moreover, the cart can be connected to a portable device like a smartphone or tablet, thus providing ease of use to the end
user. The prototype has been tested in a grocery store, while simulations have been done to analyse its scalability in larger spaces
where multiple robots could coexist.This work was partly supported by Spanish Ministry under Grant DPI2014-57746-C3 (MERBOTS Project) and by Universitat Jaume I Grants P1-1B2015-68 and PID2010-12
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