@conference {
	title = {Gibbs Sampler Based Control of Autonomous Vehicle Swarms in the Presence of Sensor Errors},
	author = {Xi, Wei., Baras, John S.},
	year = {2006},
	month = {12/2006},
	abstract = {In earlier work of the authors[1], [2], [3], [4], [5], it was shown that Gibbs sampler based annealing algorithms could be used for vehicle swarms to achieve self-organization. Nevertheless, the earlier convergence analyses were based on the assumption that the Gibbs potential can be precisely evaluated. In practice, Gibbs potentials have to be measured via sensors, which usually introduce errors. The robustness of the stochastic algorithm under sensor errors is studied in this paper. Two types of sensor error, range-error and random error, are investigated. Analytical results on convergence are derived for sensor errors with limited support, and are further validated through simulations.},
	address = {San Diego, CA},
	booktitle = {45th IEEE Conference on Decision and Control},
	pages = {pp. 5084-5090}
}
@conference {
	title = {Distributed Control of Autonomous Swarms by Using Parallel Simulated Annealing Algorithm},
	author = {Xi, Wei., Baras, John S.},
	year = {2006},
	month = {06/2006},
	abstract = {In early work of the authors, it was shown that Gibbs sampler based sequential annealing algorithm could be used to achieve self-organization in swarm vehicles based only on local information. However, long travelling time presents barriers to implement the algorithm in practice. In this paper we study a popular acceleration approach, the parallel annealing algorithm, and its convergence properties. We first study the convergence and equilibrium properties of the synchronous parallel sampling algorithm. A special example based on a battle field scenario is then studied. Sufficient conditions that the synchronous algorithm leads to desired configurations (global minimizers) are derived. While the synchronized algorithm reduces travelling time, it also raises delay and communication cost dramatically, in order to synchronize moves of a large group of vehicles. An asynchronous version of the parallel sampling algorithm is then proposed to solve the problem. Convergence properties of the asynchronous algorithm are also investigated.},
	address = {Anconia, Italy},
	booktitle = {14th Mediterranean Conference on Control and Automation},
	pages = {pp. 1-6}
}
