28,368 research outputs found
Analysis of interplanetary solar sail trajectories with attitude dynamics
We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system
An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning
High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts
Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics
In this paper we provide a thorough, rigorous theoretical framework to assess
optimality guarantees of sampling-based algorithms for drift control systems:
systems that, loosely speaking, can not stop instantaneously due to momentum.
We exploit this framework to design and analyze a sampling-based algorithm (the
Differential Fast Marching Tree algorithm) that is asymptotically optimal, that
is, it is guaranteed to converge, as the number of samples increases, to an
optimal solution. In addition, our approach allows us to provide concrete
bounds on the rate of this convergence. The focus of this paper is on mixed
time/control energy cost functions and on linear affine dynamical systems,
which encompass a range of models of interest to applications (e.g.,
double-integrators) and represent a necessary step to design, via successive
linearization, sampling-based and provably-correct algorithms for non-linear
drift control systems. Our analysis relies on an original perturbation analysis
for two-point boundary value problems, which could be of independent interest
Optimal first arrival times in L\'evy flights with resetting
We consider diffusive motion of a particle performing a random walk with
L\'evy distributed jump lengths and subject to resetting mechanism bringing the
walker to an initial position at uniformly distributed times. In the limit of
infinite number of steps and for long times, the process converges to a
super-diffusive motion with replenishment. We derive formula for a mean first
arrival time (MFAT) to a predefined target position reached by a meandering
particle and analyze efficiency of the proposed searching strategy by
investigating criteria for an optimal (a shortest possible) MFAT.Comment: 10 pages, 6 figure
Global Trajectory Optimisation : Can We Prune the Solution Space When Considering Deep Space Manoeuvres? [Final Report]
This document contains a report on the work done under the ESA/Ariadna study 06/4101 on the global optimization of space trajectories with multiple gravity assist (GA) and deep space manoeuvres (DSM). The study was performed by a joint team of scientists from the University of Reading and the University of Glasgow
Experimental exploration over a quantum control landscape through nuclear magnetic resonance
The growing successes in performing quantum control experiments motivated the
development of control landscape analysis as a basis to explain these
findings.When a quantum system is controlled by an electromagnetic field, the
observable as a functional of the control field forms a landscape. Theoretical
analyses have revealed many properties of control landscapes, especially
regarding their slopes, curvatures, and topologies. A full experimental
assessment of the landscape predictions is important for future consideration
of controlling quantum phenomena. Nuclear magnetic resonance (NMR) is exploited
here as an ideal laboratory setting for quantitative testing of the landscape
principles. The experiments are performed on a simple two-level proton system
in a HO-DO sample. We report a variety of NMR experiments roving over
the control landscape based on estimation of the gradient and Hessian,
including ascent or descent of the landscape, level set exploration, and an
assessment of the theoretical predictions on the structure of the Hessian. The
experimental results are fully consistent with the theoretical predictions. The
procedures employed in this study provide the basis for future multispin
control landscape exploration where additional features are predicted to exist
Path integral policy improvement with differential dynamic programming
Path Integral Policy Improvement with Covariance Matrix Adaptation (PI2-CMA) is a step-based model free reinforcement learning approach that combines statistical estimation techniques with fundamental results from Stochastic Optimal Control. Basically, a policy distribution is improved iteratively using reward weighted averaging of the corresponding rollouts. It was assumed that PI2-CMA somehow exploited gradient information that was contained by the reward weighted statistics. To our knowledge we are the first to expose the principle of this gradient extraction rigorously. Our findings reveal that PI2-CMA essentially obtains gradient information similar to the forward and backward passes in the Differential Dynamic Programming (DDP) method. It is then straightforward to extend the analogy with DDP by introducing a feedback term in the policy update. This suggests a novel algorithm which we coin Path Integral Policy Improvement with Differential Dynamic Programming (PI2-DDP). The resulting algorithm is similar to the previously proposed Sampled Differential Dynamic Programming (SaDDP) but we derive the method independently as a generalization of the framework of PI2-CMA. Our derivations suggest to implement some small variations to SaDDP so to increase performance. We validated our claims on a robot trajectory learning task
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