Interplanetary mission design with applications to guidance and optimal control of aero-assisted trajectories

A method for finding optimal aerogravity-assist tours of the solar system is developed using indirect methods. Two cost functionals are used in the optimization; finding the minimum required maximum lift-to-drag ratio, with and without a convective heating-rate path constraint, and the path which provides the minimum total stagnation point convective heat load. It is found that using present or near-future thermal protection system materials will suffice for certain aerogravity assist trajectories at Mars. Minimum heat load optimal trajectories are found for aerocapture maneuvers at Uranus and Neptune. With a large radius, and short rotational periods, atmospheric rotation must be taken into account to accurately model the system dynamics. Investigation of the 2018 Inspiration Mars free-return opportunity is conducted. A broad search over 100 years of Mars free-return trajectories is catalogued, and a Pareto front analysis is employed to find the overall best trajectories in the timespan. The geometry is explored further with the use of a time-free ephemeris to see where minimal energy transfer arcs between Earth and Mars occur, and see if the 2018 opportunity is one such transfer. It turned out that both the 2017 and 2064 candidates found from the 100-year search were the closest to minimum energy, highlighting the rarity of the Inspiration Mars opportunity, and gives a motivating push to fly this mission

Interplanetary mission design with applications to guidance and optimal control of aero-assisted trajectories

A method for finding optimal aerogravity-assist tours of the solar system is developed using indirect methods. Two cost functionals are used in the optimization; finding the minimum required maximum lift-to-drag ratio, with and without a convective heating-rate path constraint, and the path which provides the minimum total stagnation point convective heat load. It is found that using present or near-future thermal protection system materials will suffice for certain aerogravity assist trajectories at Mars. Minimum heat load optimal trajectories are found for aerocapture maneuvers at Uranus and Neptune. With a large radius, and short rotational periods, atmospheric rotation must be taken into account to accurately model the system dynamics. Investigation of the 2018 Inspiration Mars free-return opportunity is conducted. A broad search over 100 years of Mars free-return trajectories is catalogued, and a Pareto front analysis is employed to find the overall best trajectories in the timespan. The geometry is explored further with the use of a time-free ephemeris to see where minimal energy transfer arcs between Earth and Mars occur, and see if the 2018 opportunity is one such transfer. It turned out that both the 2017 and 2064 candidates found from the 100-year search were the closest to minimum energy, highlighting the rarity of the Inspiration Mars opportunity, and gives a motivating push to fly this mission

Absolutely free extrinsic evolution of passive low-pass filter

Evolutionary electronics is a brunch of evolvable hardware, where the evolutionary algorithm is applied towards electronic circuits. The success of evolutionary search most of all depends on variable length representation methodology. The low-pass filter is a standard task in evolutionary electronics to start with. The results of evolution enable one to qualify whether the methodology is good for further experiments. In this paper the maximum freedom for evolutionary search has been proclaimed as a main target during development of new VLR methodology. The introduction of R-support elements enables to perform an unconstrained evolution of analogue circuits for the first time. The proposed algorithm has been tested on the example of analogue low-pass filter. The experimental results demonstrate that the evolved filter is comparable with filters evolved previously using genetic programming and genetic algorithms techniques. The obtained results are compared in details with low-pass filters previously designed

Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Online quantum mixture regression for trajectory learning by demonstration

In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online Expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community

A simplified implementation of the least squares solution for pairwise comparisons matrices

This is a follow up to "Solution of the least squares method problem of pairwise comparisons matrix" by Bozóki published by this journal in 2008. Familiarity with this paper is essential and assumed. For lower inconsistency and decreased accuracy, our proposed solutions run in seconds instead of days. As such, they may be useful for researchers willing to use the least squares method (LSM) instead of the geometric means (GM) method

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.Comment: 10 page

Recurrent Latent Variable Networks for Session-Based Recommendation

In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques