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Many topology control methods for autonomous mobile vehicles assume exact knowledge of the locations of neighboring nodes to make meaningful movement decisions. We present our node-spreading Voronoi algorithm (nsva) and node-spreading Voronoi-based genetic algorithm (nsvga), for self-positioning autonomous nodes in noisy environments. The performance of nsva and nsvga were evaluated in simulation experiments by measuring the network area coverage, average distance traveled and number of disconnected nodes. Experimental results show that both nsva and nsvga can adequately cover the deployment area despite errors in neighbor location information. nsvga can tolerate location errors and maintain network connectivity better than nsva at the cost of increased movement.

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