@conference {
	title = {A Stochastic Algorithm for Self-Organization of Autonomous Swarms},
	author = {Xi, Wei., Tan, Xiaobo., Baras, John S.},
	year = {2005},
	month = {12/2005},
	abstract = {In earlier work of the authors simulation results indicated the possibility of achieving self-organization of autonomous vehicles through Gibbs sampler-based simulated annealing. However, the dynamic graph structure associated with the network evolution presents challenges in convergence analysis. In this paper a novel algorithm is presented and shown to yield desired global configurations with primarily local interactions. Its convergence speed is provided in terms of the Gibbs potential function. The analytical results are further verified through simulation.},
	address = {Seville, Spain},
	booktitle = {44th IEEE Conference on Decision and Control and the European Control Conference (CDC-ECC {\textquoteright}05)},
	pages = {pp. 765-770}
}
@conference {
	title = {Gibbs Sampler-Based Path Planning for Autonomous Vehicles: Convergence analysis},
	author = {Xi, Wei., Tan, Xiaobo., Baras, John S.},
	year = {2005},
	month = {07/2005},
	abstract = {Simulation has indicated that distributed self-organization of autonomous swarms might be achieved through Gibbs sampler-based simulated annealing. However, the dynamic nature of the underlying graph presents significant challenges in convergence analysis. As a first step toward such analysis, convergence of the algorithm is established in this paper for two special cases: single vehicle with limited sensing/moving range, and multiple vehicles with full sensing/moving range. The impact of Gibbs potential functions on the convergence speed is also investigated, which provides insight into the design of these functions.},
	address = {Prague, Czech Republic},
	booktitle = {IFAC 2005}
}
@conference {
	title = {A Hybrid Scheme for Distributed Control of Autonomous Swarms},
	author = {Xi, Wei., Tan, Xiaobo., Baras, John S.},
	year = {2005},
	month = {06/2005},
	abstract = {In this paper, a hybrid scheme for distributed control of autonomous vehicles is presented by combining the deterministic gradient-flow method and the stochastic method based on the Gibbs sampler. The scheme has the advantages of both methods and can potentially provide fast, distributed maneuvers while avoiding getting trapped at local minima of the potential function. Preliminary analysis is performed for the optimal design of the parameters controlling the switching between the two methods. The performance of the hybrid scheme is further enhanced by the introduction of vehicle memory. Simulation results are provided to confirm the analysis and show the effectiveness of the proposed algorithm.},
	address = {Portland USA},
	booktitle = {American Control Conference},
	pages = {pp. 3486-3491}
}
