This paper describes the orbit design of the deployable payload Rover 2 of MINERVA-II,installed on the Hayabusa2 spacecraft.Because Rover 2 did not have surface exploration capabilities,the operation team decided to e...This paper describes the orbit design of the deployable payload Rover 2 of MINERVA-II,installed on the Hayabusa2 spacecraft.Because Rover 2 did not have surface exploration capabilities,the operation team decided to experiment with a new strategy for its deployment to the surface.The rover was ejected at a high altitude and made a semi-hard landing on the surface of the asteroid Ryugu after several orbits.Based on the orbital analysis around Ryugu,the expected collision speed was tolerable for the rover to function post-impact.Because the rover could not control its position,its motion was entirely governed by the initial conditions.Thus,the largest challenge was to insert the rover into a stable orbit(despite its large release uncertainty),and avoid its escape from Ryugu due to an environment strongly perturbed by solar radiation pressure and gravitational irregularities.This study investigates the solution space of the orbit around Ryugu and evaluates the orbit’s robustness by utilizing Monte Carlo simulations to determine the orbit insertion policy.Upon analyzing the flight data of the rover operation,we verified that the rover orbited Ryugu for more than one period and established the possibility of a novel method for estimating the gravity of an asteroid.展开更多
This paper demonstrates how artificial neural networks can be used to alleviate common problems encountered when creating a large database of Poincar´e map responses.A general architecture is developed using a co...This paper demonstrates how artificial neural networks can be used to alleviate common problems encountered when creating a large database of Poincar´e map responses.A general architecture is developed using a combination of regression and classification feedforward neural networks.This allows one to predict the response of the Poincar´e map,as well as to identify anomalies,such as impact or escape.Furthermore,this paper demonstrates how an artificial neural network can be used to predict the error between a more complex and a simpler dynamical system.As an example application,the developed architecture is implemented on the Sun-Mars eccentric Hill system.Error statistics of the entire architecture are computed for both one Poincar´e map and for iterated maps.The neural networks are then applied to study the long-term impact and escape stability of trajectories in this system.展开更多
基金This work was partially supported by JSPS KAKENHI Grant(No.18H01628).
文摘This paper describes the orbit design of the deployable payload Rover 2 of MINERVA-II,installed on the Hayabusa2 spacecraft.Because Rover 2 did not have surface exploration capabilities,the operation team decided to experiment with a new strategy for its deployment to the surface.The rover was ejected at a high altitude and made a semi-hard landing on the surface of the asteroid Ryugu after several orbits.Based on the orbital analysis around Ryugu,the expected collision speed was tolerable for the rover to function post-impact.Because the rover could not control its position,its motion was entirely governed by the initial conditions.Thus,the largest challenge was to insert the rover into a stable orbit(despite its large release uncertainty),and avoid its escape from Ryugu due to an environment strongly perturbed by solar radiation pressure and gravitational irregularities.This study investigates the solution space of the orbit around Ryugu and evaluates the orbit’s robustness by utilizing Monte Carlo simulations to determine the orbit insertion policy.Upon analyzing the flight data of the rover operation,we verified that the rover orbited Ryugu for more than one period and established the possibility of a novel method for estimating the gravity of an asteroid.
基金This work utilized the RMACC Summit supercomputer,which is supported by the National Science Foundation(awards ACI-1532235 and ACI-1532236)the University of Colorado Boulder,and Colorado State University.
文摘This paper demonstrates how artificial neural networks can be used to alleviate common problems encountered when creating a large database of Poincar´e map responses.A general architecture is developed using a combination of regression and classification feedforward neural networks.This allows one to predict the response of the Poincar´e map,as well as to identify anomalies,such as impact or escape.Furthermore,this paper demonstrates how an artificial neural network can be used to predict the error between a more complex and a simpler dynamical system.As an example application,the developed architecture is implemented on the Sun-Mars eccentric Hill system.Error statistics of the entire architecture are computed for both one Poincar´e map and for iterated maps.The neural networks are then applied to study the long-term impact and escape stability of trajectories in this system.
