A probabilistic approach to collaborative multi-robot localization pdf

Keyphrases collaborative multi-robot localization probabilistic approach mobile robot probabilistic method multiple robot platform localization speed sample-based version successful localization heterogeneous robot any-time fashion high-cost sensor conventional single-robot localization collaborative mobile robot localization simulation run robot belief certain condition drastic improvement statistical algorithm markov localization real robot.

Powered by:. Create a New Binder Name. Cancel Create. Autonomous Robots Volume 8, Issue 3. Previous Article Next Article. Index Terms auto-classified. Computing methodologies. Login options Check if you have access through your login credentials or your institution to get full access on this article. Sign in. Full Access Get this Article. Information Contributors Published in. ISSN: Kluwer Academic Publishers United States.

Author Tags mobile robots localization uncertainty multi-robot systems. Qualifiers article. This paper presents a statistical algorithm for collaborative mobile robot localization.

Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another.

As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.

A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.

Autonomous Robots — Springer Journals. Continue with Facebook. Sign up with Google. Log in with Microsoft. Bookmark this article. You can see your Bookmarks on your DeepDyve Library. Sign Up Log In. Copy and paste the desired citation format or use the link below to download a file formatted for EndNote. Conclusions Teams of multiple mobile robots can be effectively applied to numerous applications , such as space exploration, underground mining, and building security.

Compared to individual robots, … Expand. View 4 excerpts, references background and methods. Active Markov localization for mobile robots. Robotics Auton. Monte Carlo localization for mobile robots. View 3 excerpts, references results, methods and background. View 5 excerpts, references background, methods and results. Active Mobile Robot Localization.

View 1 excerpt, references methods. Integrating global position estimation and position tracking for mobile robots: the dynamic Markov localization approach. Innovations in Theory, Practice and Applications Cat. Markov Localization using Correlation. Highly Influential.