Robot Teams: From Diversity to Polymorphism
Robot Teams: From Diversity to Polymorphism
The Legion System: A Novel Approach to Evolving Hetrogeneity for Collective Problem Solving
Proceedings of the European Conference on Genetic Programming
Rescue robotics for homeland security
Communications of the ACM - Homeland security
International Journal of Robotics Research
Heterogeneous multirobot coordination with spatial and temporal constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Heterogeneous task allocation and team design for multiagent routing domains
Heterogeneous task allocation and team design for multiagent routing domains
COBOS: Cooperative backoff adaptive scheme for multirobot task allocation
IEEE Transactions on Robotics
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Many realistic problem domains are composed of heterogeneous tasks distributed in a physical environment. Even though the distribution of skills among the members of a heterogeneous team has a significant influence on its effectiveness, little is known about how to design effective heterogeneous teams. In this paper, we develop a graph-search approach to tackle this team design problem in the context of multiagent routing, a generalizable domain in which heterogeneous, randomly located tasks must be completed in overall minimum time (or make span) given an a priori distribution of their heterogeneity, a fixed team size, and a limited budget. First, we develop complete and optimal search algorithms. Second, we show that dominance-based pruning significantly increases the size of problems that can be solved optimally. Third, we introduce an anytime algorithm called TD-BR that uses beam search with restarts in order to scale up to much larger problems. We evaluate our algorithms empirically in two ways: first, we predict the performance of the teams using a team performance metric called task coverage, and show that our algorithms produce high coverage teams, second, we test a subset of these teams in simulation by allocating the teams to various task sets and measuring their make span. We show that our teams perform well when compared to an ideal homogeneous team, and outperform heterogeneous teams created by other methods. Our main contributions are thus new algorithmic tools for designers of heterogeneous teams in robotics and other domains where modular construction and refitting of robots is possible.