Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm
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<strong>Session</strong> WedBT7 Vega <strong>Wednesday</strong>, <strong>October</strong> <strong>10</strong>, <strong>2012</strong>, 09:30–<strong>10</strong>:30<br />
Multiple Mobile Robot Planning II<br />
Chair Ronald Arkin, Georgia Tech.<br />
Co-Chair<br />
09:30–09:45 WedBT7.1<br />
Goal Assignment using Distance Cost<br />
in Multi-Robot Exploration<br />
Jan Faigl Miroslav Kulich Libor Přeučil<br />
Department of Cybernetics<br />
Czech Technical University in Prague, Czech Republic<br />
• Multi-robot exploration strategies<br />
• Performance evaluation of: (1) greedy<br />
assignment; (2) iterative assignment; (3)<br />
Hungarian algorithm; and (4) multiple traveling<br />
salesman (MTSP) approaches<br />
• MTSP based assignment with (cluster first,<br />
route second)<br />
• Clustering of the goal candidates preserving<br />
geodesic distances<br />
<strong>10</strong>:00–<strong>10</strong>:15 WedBT7.3<br />
Finding Graph Topologies for Feasible<br />
Multirobot Motion Planning<br />
Pushkar Kolhe, Henrik I. Christensen<br />
•For a Kiva-like warehousing<br />
scenario:<br />
1.Where should I place n robots?<br />
2.Can we ensure<br />
deadlock/collision free motion<br />
planning from these n places?<br />
•An Integer Programming formulation<br />
to find a graph for solving multirobot<br />
motion planning problems<br />
Goal Nodes<br />
Robot Nodes<br />
09:45–<strong>10</strong>:00 WedBT7.2<br />
Multi-Agent Generalized Probabilistic<br />
RoadMaps (MAGPRM)<br />
Sandip Kumar and Suman Chakravorty<br />
Texas A&M University, College Station, USA<br />
MAGPRM is a sampling based method for<br />
planning the motion of multiple agents under<br />
process uncertainty, workspace constraints,<br />
and non-trivial dynamics<br />
MAGPRM utilizes the GPRM, a sampling based<br />
method for planning the motion of a single agent<br />
under process uncertainty, and a Multiple<br />
Traveling Salesman Problem (MTSP) solver.<br />
MAGPRM guarantees performance in terms of<br />
a maximum allowable probability of failure for<br />
the agents<br />
<strong>10</strong>:15–<strong>10</strong>:30 WedBT7.4<br />
Dynamic Positioning of Beacon Vehicles for<br />
Cooperative Underwater Navigation<br />
Alexander Bahr and Alcherio Martinoli<br />
Distributed Intelligent Systems and Algorithms Lab, EPFL, Switzerland<br />
John J. Leonard<br />
Department of Mechanical Engineering, MIT, USA<br />
• Beacon vehicles serve as navigation aids<br />
for submerged AUVs<br />
• Our algorithm optimizes the beacon<br />
vehicles’ position to improve the AUVs’<br />
navigation accuracy<br />
• No a-priori information, such as the AUVs’<br />
mission plan, required<br />
• Distributed algorithm adapts to group size<br />
and connectivity<br />
<strong>2012</strong> IEEE/RSJ International Conference on Intelligent Robots and Systems<br />
–134–
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