As an alternative to cumbersome aerial vehicles with considerable maintenance requirements and flight envelope restrictions, the X4 flyer is chosen as the basis for the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC). This paper outlines the design and development of a miniature autonomous waypoint tracker flight control system, and the creation of a multi-vehicle platform for experimentation and validation of multi-agent control algorithms. This testbed development paves the way for real-world implementation of recent work in the fields of autonomous collision and obstacle avoidance, task assignment formation flight, using both centralized and decentralized techniques.

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve distributed optimization and control problems. This is done by translating the distributed problem into an iterated game, where each agent's mixed strategy (i.e. its stochastically determined move) sets a different variable of the problem. So the expected value of the objective function of the distributed problem is determined by the joint probability distribution across the moves of the agents. The mixed strategies of the agents are updated from one game iteration to the next so as to converge on a joint distribution that optimizes that expected value of the objective function. Here, a set of new techniques for this updating is presented. These and older techniques are then extended to apply to uncountable move spaces. We also present an extension of the approach to include (in)equality constraints over the underlying variables. Another contribution is that we show how to extend the Monte Carlo version of the approach to cases where some agents have no Monte Carlo samples for some of their moves, and derive an "automatic annealing schedule".

The use of expensive simulations in engineering design optimization often rules out conventional techniques for design optimization for a variety of reasons, such as lack of smoothness, unavailability of gradient information, presence of multiple local optima, and most importantly, limits on available computing resources and time. Often, the designer also has access to lower-fidelity simulations that may suffer from poor accuracy in some regions of the design space, but are much cheaper to evaluate than the original expensive simulation. We can accelerate the design process by efficiently managing these models of various fidelities. There has been previous research in this area: some algorithms in the literature first estimate of the relationships between these models, and then perform optimization on the corrected low-fidelity models. Others adaptively select new high-fidelity designs, but these usually require gradient information; those that relax this requirement use a trust-region-based local search method. In contrast, most global optimization methods in the literature require smoothness, and do not incorporate multifidelity analyses.We would like to combine the advantages of all these techniques, and in this paper, we describe a method to incorporate models of two fidelities and perform a gradient-free global search on expensive functions that are not necessarily smooth everywhere. The main contribution of this paper is an extension of the well-known technique of maximization of expected improvement to the two-fidelity case. We demonstrate this improved technique on some academic problems with an artificially constructed 'low-fidelity' approximation, and also on a simple application problem in supersonic design optimization.

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