1,102 research outputs found
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings
in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Evaluation of Using Semi-Autonomy Features in Mobile Robotic Telepresence Systems
Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Plan Nacional de Investigación, proyecto DPI2011-25483
An Analytical Approach to Avoid Obstacles in Mobile Robot Navigation
A nonlinear supervised globally stable controller is proposed to reactively guide a mobile robot to avoid obstacles while seeking a goal. Whenever the robot detects an object nearby, its orientation is changed to be aligned with the tangent to the border of the obstacle. Then, the robot starts following it, looking for a feasible path to its goal. The supervisor is responsible for deciding which path to take when the robot faces some particular obstacle configurations that are quite difficult to deal with. Several simulations and experiments were run to validate the proposal, some of which are discussed here. To run the experiments, the proposed controller is programmed into the onboard computer of a real unicycle mobile platform, equipped with a laser range scanner. As for the simulations, the models of the same experimental setup were used. The final conclusion is that the nonlinear supervised controller proposed to solve the problem of avoiding obstacles during goal seeking has been validated, based on the theoretical analysis, and the simulated and experimental results.Fil: Brandao, Alexandre Santos. Federal University of Viçosa. Department of Electrical Engineering; BrasilFil: Sarcinelli Filho, Mário . Federal University of Espírito Santo. Department of Electrical Engineering; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingenieria. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Developing a safe and efficient collision avoidance policy for multiple
robots is challenging in the decentralized scenarios where each robot generate
its paths without observing other robots' states and intents. While other
distributed multi-robot collision avoidance systems exist, they often require
extracting agent-level features to plan a local collision-free action, which
can be computationally prohibitive and not robust. More importantly, in
practice the performance of these methods are much lower than their centralized
counterparts.
We present a decentralized sensor-level collision avoidance policy for
multi-robot systems, which directly maps raw sensor measurements to an agent's
steering commands in terms of movement velocity. As a first step toward
reducing the performance gap between decentralized and centralized methods, we
present a multi-scenario multi-stage training framework to find an optimal
policy which is trained over a large number of robots on rich, complex
environments simultaneously using a policy gradient based reinforcement
learning algorithm. We validate the learned sensor-level collision avoidance
policy in a variety of simulated scenarios with thorough performance
evaluations and show that the final learned policy is able to find time
efficient, collision-free paths for a large-scale robot system. We also
demonstrate that the learned policy can be well generalized to new scenarios
that do not appear in the entire training period, including navigating a
heterogeneous group of robots and a large-scale scenario with 100 robots.
Videos are available at https://sites.google.com/view/drlmac
Autonomous control of underground mining vehicles using reactive navigation
Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine
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