In this paper we propose search strategies for heterogeneous multi-agent
systems. Multiple agents, equipped with communication gadget, computational
capability, and sensors having heterogeneous capabilities, are deployed in the
search space to gather information such as presence of targets. Lack of
information about the search space is modeled as an uncertainty density
distribution. The uncertainty is reduced on collection of information by the
search agents. We propose a generalization of Voronoi partition incorporating
the heterogeneity in sensor capabilities, and design optimal deployment
strategies for multiple agents, maximizing a single step search effectiveness.
The optimal deployment forms the basis for two search strategies, namely, {\em
heterogeneous sequential deploy and search} and {\em heterogeneous combined
deploy and search}. We prove that the proposed strategies can reduce the
uncertainty density to arbitrarily low level under ideal conditions. We provide
a few formal analysis results related to stability and convergence of the
proposed control laws, and to spatial distributedness of the strategies under
constraints such as limit on maximum speed of agents, agents moving with
constant speed and limit on sensor range. Simulation results are provided to
validate the theoretical results presented in the paper.