Adaptive optics applies advanced sensing and control to improve the ability of optical systems to collect images
through a turbulent atmosphere. The results of this research effort demonstrate that the combination of two
recent approaches improves the performance of adaptive optics in directed energy and laser communication
scenarios. The first approach is adaptive control, which offers improved performance over fixed-gain controllers
in the presence of rapidly changing turbulence. The second approach incorporated into the study is a dual-mirror
system. The two mirrors are a high-bandwidth, low-actuator-stroke (tweeter) mirror and a low-bandwidth,
large-actuator-stroke (woofer) mirror. The woofer-tweeter combination allows for better compensation of the
large-variance, high-spatial-frequency phase distortion generated by strong turbulence. Two different adaptive
controllers are presented, one using a relatively simple model reference adaptive system controller and one using a
lattice filter controller. The lattice filter is implemented in two ways. In one implementation the filter operates on
the individual actuators, while in the other it operates on frequency-weighted modes. The modal implementation
reduces the computational burden of the filter. The performance of the different adaptive controllers is compared
to both each other and to a traditional fixed-gain controller. Simulations show that adaptive control of woofertweeter
adaptive optics can increase the mean Strehl ratio by up to 20%. In general, the lattice filter controllers
outperform the model reference adaptive system controller. However, in cases where the lattice filter cannot use
a sufficient number of modes, the model reference adaptive system can outperform the lattice filter.
In this paper, we studied the trajectory generation problem for a two-degrees-of-freedom robot in a workspace with obstacles. To generate the robot's trajectories, we developed a genetic algorithm to search for valid solutions in the configuration space. Our results present a novel perspective on the problem not seen in the conventional robot trajectory planners. The genetic algorithm approach is beneficial because it may be extended to plan trajectories for robots with more degrees of freedom. The evolutionary search process may allow the user to solve the trajectory problem in an n-dimensional space where the 'curse of dimensionality' inevitably stalls conventional methods. We demonstrate the algorithm with some examples and discuss the possible extension to higher order problems.
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