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We consider the problem of determining the optimal composition of aheterogeneous multi-agent team for coverage problems by including costsassociated with different agents and subject to an upper bound on the maximalallowable number of agents. We formulate a resource allocation problem withoutintroducing additional non-convexities to the original problem. We develop adistributed Projected Gradient Ascent (PGA) algorithm to solve the optimal teamcomposition problem. To deal with non-convexity, we initialize the algorithmusing a greedy method and exploit the submodularity and curvature properties ofthe coverage objective function to derive novel tighter performance boundguarantees on the optimization problem solution. Numerical examples areincluded to validate the effectiveness of this approach in diverse missionspace configurations and different heterogeneous multi-agent collections.Comparative results obtained using a commercial mixed-integer nonlinearprogramming problem solver demonstrate both the accuracy and computationalefficiency of the distributed PGA algorithm.

Optimal Composition of Heterogeneous Multi-Agent Teams for Coverage Problems with Performance Bound Guarantees