Adaptive Pinpoint and Fuel Efficient Mars Landing Using Reinforcement Learning

Future unconstrained and science-driven missions to Mars will require advanced guidance algorithms that are able to adapt to more demanding mission requirements, e.g. landing on selected locales with pinpoint accuracy while autonomously flying fuel-efficient trajectories. In this paper, a novel guidance algorithm designed by applying the principles of reinforcement learning(RL) theory is presented. The goal is to devise an adaptive guidance algorithm that enables robust, fuel efficient,and accurate landing without the need for off line trajectory generation and real-time tracking. Results from a Monte Carlo simulation campaign show that the algorithm is capable of autonomously following trajectories that are close to the optimal minimum-fuel solutions with an accuracy that surpasses that of past and future Mars missions. The proposed RL-based guidance algorithm exhibits a high degree of flexibility and can easily accommodate autonomous retargeting while maintaining accuracy and fuel efficiency. Although reinforcement learning and other similar machine learning techniques have been previously applied to aerospace guidance and control problems(e.g., autonomous helicopter control), this appears, to the best of the authors knowledge, to be the first application of reinforcement learning to the problem of autonomous planetary landing.

Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization

Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of options.An unguided search by experimental investigations or molecular simulations is prohibitively expensive.In this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or not.We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive search.We find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach.This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.

Extending HTN to planning and execution control for small combat unit simulation

Modeling how military commanders carry out operations is considered complicated,requiring the capability of not only planning for multiple subordinates but also responding to unexpected events during execution.This paper presents an Hierarchical Task Network(HTN)embedded planning and execution control architecture for small unit commander agents.To be adaptive to dynamic world state changes,the architecture employs a partial planning mechanism and generates actions only applicable to current situations.It is also able to coordinate subordinates’actions and handle execution failures at runtime.We demonstrate the architecture’s use with an infantry company scenario,where the commander orders three platoons assaulting a defined hill.Our approach shows the effectiveness to control multiple entities in dynamic environments,making the architecture well-suited to represent small unit commanders’behavior.

On social computing research collaboration patterns： a social network perspective

The field of social computing emerged more than ten years ago. During the last decade, researchers from a vari- ety of disciplines have been closely collaborating to boost the growth of social computing research. This paper aims at iden- tifying key researchers and institutions, and examining the collaboration patterns in the field. We employ co-authorship network analysis at different levels to study the bibliographic information of 6 543 publications in social computing from 1998 to 2011. This paper gives a snapshot of the current re- search in social computing and can provide an initial guid- ance to new researchers in social computing.