This video shows two mutual localization experiments performed on a team of 5 Khepera III mini-robots equipped with laser range-finders. In the first experiment all robots participate to the mutual localization process, while in the second experiment two robots do not communicate their data to the others, and therefore are identified by the filter as moving obstacles that "look like robots".
Mutual Localization with Position Measures (MLPM) is the problem of determining all the robot-to-robot relative locations (position and orientation) within a group of sensing agents, using relative position (range and bearing) measures taken by on-board sensors. Solving this problem is essential to perform cooperative multi-robot tasks, such as coverage, deployment, exploration, map building, formation control, surveillance, monitoring, escorting and entrapment. Mutual Localization with Anonymous Position Measures (MLAPM) is an extension of MLPM with the additional assumption that the sensor cannot distinguish one robot from the other. For certain configurations of the multi-robot system, the anonymity hypothesis causes a combinatorial ambiguity in the inversion of the measurement equation, resulting in the existence of multiple solutions. We have developed a two-phase filter for solving the MLAPM problem. The first phase uses MultiReg, an innovative algorithm aimed at obtaining sets of geometrically feasible relative pose hypotheses. Then, its output is processed by a data associator and a multiple EKF to rate and select the best hypothesis.
See also the mutual localization page for an overview on the topic.
- , “Probabilistic Mutual Localization in Multi-agent Systems from Anonymous Position Measures”, in 49th IEEE Conference on Decision and Control, Atlanta, GA, USA, 2010, pp. 6534-6540.
- , “On the Solvability of the Mutual Localization Problem with Anonymous Position Measures”, in 2010 IEEE Int. Conf. on Robotics and Automation, Anchorage, AK, 2010, pp. 3193-3199.
- , “Mutual Localization in a Multi-Robot System with Anonymous Relative Position Measures”, in 2009 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, St. Louis, MO, 2009, pp. 3974-3980.
- , “Mutual Localization in a Multi-Robot System with Anonymous Relative Position Measures”, Department of Computer and System Sciences Antonio Ruberti, 2009.