Abstract
This letter proposes a routing framework for heterogeneous multi-robot teams in exploration tasks. The proposed framework deals with a combinatorial optimization problem, and provides a new solving algorithm, for Generalized Team Orienteering Problem (GTOP). In this letter, a route optimization problem is formulated for a heterogeneous multi-robot system. A novel problem solver is also proposed based on self-organizing map. The proposed framework has a strong advantage in its scalability because the processing time is independent from the number of robots, and the heterogeneity of the team. The validity of the proposed framework is evaluated in the exploration, and mapping tasks by heterogeneous robot team with overlapping abilities. The simulation results show the effectiveness of the proposed framework, and how it outperforms the conventional greedy exploration scheme.
Highlights
T ECHNICAL barrier to developing small and cheap robots is dramatically lowered recently thanks to the improvement of electronics
Researches on multi-robot systems (MRS) gains strong attention aiming for practical applications
Such problems have been studied as multi-robot task allocation problem (MRTA), and several frameworks have been proposed [5], [6], [7]
Summary
Abstract—This letter proposes a routing framework for heterogeneous multi-robot teams in exploration tasks. The proposed framework deals with a combinatorial optimization problem, and provides a new solving algorithm, for Generalized Team Orienteering Problem (GTOP). A route optimization problem is formulated for a heterogeneous multi-robot system. A novel problem solver is proposed based on self-organizing map. The proposed framework has a strong advantage in its scalability because the processing time is independent from the number of robots, and the heterogeneity of the team. The validity of the proposed framework is evaluated in the exploration, and mapping tasks by heterogeneous robot team with overlapping abilities. The simulation results show the effectiveness of the proposed framework, and how it outperforms the conventional greedy exploration scheme
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