@conference {
	title = {MPC Based Motion Control of Car-like Vehicle Swarms},
	author = {Xi, Wei., Baras, John S.},
	year = {2007},
	month = {06/2007},
	abstract = {In this paper we study low level motion control of car-like vehicles for applications of UAV swarms. First, a Suboptimal Continuous-Curvature trajectory generation approach is studied to generate suboptimal reference trajectories(tracks) to connect desired way-point sequences. Two Model Predictive Control (MPC) based track following control approaches are then proposed to deal with the multiple constraints present in practice; for example, actuator saturation,local collision avoidance. Simulations further confirm the motion control design.},
	address = {Athens, Greece},
	booktitle = {Proceedings of 15th IEEE Mediterranean Conference on Control and Automation},
	pages = {pp. 1-6}
}
@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}
}
@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}
}
@conference {
	title = {Numerical Study on Joint Quantization and Control Under Block-Coding},
	author = {Tan, Xiaobo., Xi, Wei., Baras, John S.},
	year = {2004},
	month = {12/2004},
	abstract = {This paper addresses the joint quantization and control problem for hidden Markov chains with variable-length block-coding. The aim is to understand the impact of communication bandwidth and information delay (due to block-coding) on the control performance. A heuristic algorithm is developed to solve the dynamic programming (DP) equation through the introduction of a metric on the discrete observation space. Numerical results are presented demonstrating the attention division in block-coding and the tradeoffs between control performance and communication bandwidth.},
	address = {Atlantis, Paradise Island, Bahamas},
	booktitle = {43rd IEEE Conference on Decision and Control},
	pages = {pp. 4515-4520}
}
@conference {
	title = {Jointly optimal quantization, estimation, and control of hidden Markov chains},
	author = {Baras, John S., Tan, Xiaobo., Xi, Wei.},
	year = {2003},
	month = {12/2003},
	abstract = {It is of interest to understand the tradeoff between the communication resource consumption and the achievable system performance in networked control systems. In this paper we explore a general framework for tradeoff analysis and decision making in such systems by studying joint quantization, estimation, and control of a hidden Markov chain. Dynamic programming is used to find the optimal quantization and control scheme that minimizes a weighted combination of different cost terms including the communication cost, the delay, the estimation error, and the running cost. Simulation and analysis based on example problems show that this approach is able to capture the tradeoffs among competing objectives by adjusting the cost weights.},
	address = {Maui, Hawaii},
	booktitle = {42nd IEEE Conference on Decision and Control},
	pages = {pp. 1098-1103}
}
@article {
	title = {Decentrailzed Coordination of Autonomous Swarms Using Parallel Gibbs Sampling},
	author = {Tan, Xiaobo., Xi, Wei., Baras, John S.},
	year = {2010},
	month = {12/2010},
	abstract = {In this paper, we present analysis of a discrete-time, decentralized, stochastic coordination algorithm for a group of mobile nodes, called an autonomous swarm, on a finite spatial lattice. All nodes take their moves by sampling in parallel their locally perceived Gibbs distributions corresponding to a pairwise, nearest-neighbor potential. The algorithm has no explicit requirements on the connectedness of the underlying information graph, which varies with the swarm configuration. It is established that, with an appropriate annealing schedule, the algorithm results in swarm configurations converging to the (global) minimizers of a modified potential energy function. The extent of discrepancy between the modified and original potential energy functions is determined by the maximum node travel between time steps, and when such distance is small, the ultimate swarm configurations are close to the global minimizers of the original potential energy. Simulation results are further presented to illustrate the capability of the sampling algorithm in approximate global optimization for swarms.},
	pages = {pp. 2068-2076},
	volume = {Volume 46},
	number = {Issue 12},
	journal = {Automatica}
}
@article {
	title = {Gibbs Sampler-Based Self-Organization of Autonomous Swarms},
	author = {Xi, Wei., Tan, Xiaobo., Baras, John S.},
	year = {2006},
	month = {07/2006},
	abstract = {In this paper a novel, Gibbs sampler-based algorithm is proposed for coordination of autonomous swarms. The swarm is modeled as a Markov random field (MRF) on a graph with a time-varying neighborhood system determined by local interaction links. The Gibbs potential is designed to reflect global objectives and constraints. It is established that with primarily local sensing/communications, the swarm configuration converges to the global minimizer(s) of the potential function. The impact of the Gibbs potential on the convergence speed is investigated. Finally, a hybrid algorithm is developed to improve the efficiency of the stochastic scheme by integrating the Gibbs sampler-based method with the deterministic gradient-flow method. Simulation results are presented to illustrate the proposed approach and verify the analyses.},
	pages = {pp.  1107-1119},
	volume = {Vol. 42},
	number = {Issue 7},
	journal = {Automatica}
}
@techreport {
	title = {Jointly Optimal Quantization, Estimation, and Control of Hidden Markov Chains},
	author = {Tan, Xiaobo., Xi, Wei.},
	year = {2003},
	month = {01/2003},
	abstract = {It is of interest to understand the tradeoff between the communication resource comsumption and the achievable system performance in networked control systems. In this paper we explore a general framework for trade-off analysis and decision making in such systems by studying joint quantization,estimation, and control of a hidden Markov chain. We first formulate the joint quantization and estimation problem, where vector quantization with variable-block length is considered. Dynamic programming (DP) is used to find the optimal quantization scheme that minimizes a weighted combination of the estimation error, the communication cost, and the delay due to block coding. The DP equation issolved numerically and simulation shows that this approach is able to capture the tradeoffs among competing objectives by adjusting the cost weights. We then study the joint quantization and control problem. An example problem is solved analytically, which provides interesting insight into the approach. In both the joint quantization/estimation problem and the joint quantization/control problem, we show that the separation principle holds. The approaches to solving these two problemsshare the same spirit, and can be combined and extended to accomodate more objectives.},
	number = {Number: CSHCN TR 2003-9}
}
