Waypoint Constrained Multi-Phase Optimal Guidance of Spacecraft for Soft Lunar Landing

2019 ◽  
Vol 07 (02) ◽  
pp. 83-104 ◽  
Author(s):  
Kapil Sachan ◽  
Radhakant Padhi
Keyword(s):  
Selection Procedure ◽  
Landing Site ◽  
Terminal Point ◽  
Computationally Efficient ◽  
Soft Landing ◽  
Hard Constraints ◽  
Optimal Guidance ◽  
Safety Constraints ◽  
Multi Phase ◽  
Time Selection

A waypoint constrained multi-phase nonlinear optimal guidance scheme is presented in this paper for the soft landing of a spacecraft on the Lunar surface by using the recently developed computationally efficient Generalized Model Predictive Static Programming (G-MPSP). The proposed guidance ensures that the spacecraft passes through two waypoints, which is a strong requirement to facilitate proper landing site detection by the on-board camera for mission safety. Constraints that are required at the waypoints as well as at the terminal point include position, velocity, and attitude of the spacecraft. In addition to successfully meeting these hard constraints, the G-MPSP guidance also minimizes the fuel consumption, which is a very good advantage. An optimal final time selection procedure is also presented in this paper to facilitate minimization of fuel requirement to the best extent possible. Extensive simulation studies have been carried out with various perturbations to illustrate the effectiveness of the algorithm. Finally, processor-in-loop simulation has been carried out, which demonstrates the feasibility of on-board implementation of the proposed guidance.

Multi-phase MPSP Guidance for Lunar Soft Landing

2020 ◽  
Vol 5 (1) ◽  
pp. 61-74
Author(s):  
Avijit Banerjee ◽  
Radhakant Padhi
Keyword(s):  
Soft Landing ◽  
Multi Phase

scholarly journals PSO-Based Soft Lunar Landing with Hazard Avoidance: Analysis and Experimentation

Aerospace ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 195
Author(s):  
Andrea D’Ambrosio ◽  
Andrea Carbone ◽  
Dario Spiller ◽  
Fabio Curti
Keyword(s):  
Real Time ◽  
Inverse Dynamics ◽  
A Priori ◽  
Closed Form Solution ◽  
Control Policy ◽  
Form Solution ◽  
Soft Landing ◽  
Lunar Landing ◽  
Optimal Guidance ◽  
Time Optimal

The problem of real-time optimal guidance is extremely important for successful autonomous missions. In this paper, the last phases of autonomous lunar landing trajectories are addressed. The proposed guidance is based on the Particle Swarm Optimization, and the differential flatness approach, which is a subclass of the inverse dynamics technique. The trajectory is approximated by polynomials and the control policy is obtained in an analytical closed form solution, where boundary and dynamical constraints are a priori satisfied. Although this procedure leads to sub-optimal solutions, it results in beng fast and thus potentially suitable to be used for real-time purposes. Moreover, the presence of craters on the lunar terrain is considered; therefore, hazard detection and avoidance are also carried out. The proposed guidance is tested by Monte Carlo simulations to evaluate its performances and a robust procedure, made up of safe additional maneuvers, is introduced to counteract optimization failures and achieve soft landing. Finally, the whole procedure is tested through an experimental facility, consisting of a robotic manipulator, equipped with a camera, and a simulated lunar terrain. The results show the efficiency and reliability of the proposed guidance and its possible use for real-time sub-optimal trajectory generation within laboratory applications.

scholarly journals Information-Maximization Clustering Based on Squared-Loss Mutual Information

2014 ◽  
Vol 26 (1) ◽  
pp. 84-131 ◽  
Author(s):  
Masashi Sugiyama ◽  
Gang Niu ◽  
Makoto Yamada ◽  
Manabu Kimura ◽  
Hirotaka Hachiya
Keyword(s):  
Mutual Information ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Selection Procedure ◽  
Eigenvalue Decomposition ◽  
Model Parameters ◽  
Computationally Efficient ◽  
Novel Approach ◽  
Information Maximization

Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it involves only continuous optimization of model parameters, which is substantially simpler than discrete optimization of cluster assignments. However, existing methods still involve nonconvex optimization problems, and therefore finding a good local optimal solution is not straightforward in practice. In this letter, we propose an alternative information-maximization clustering method based on a squared-loss variant of mutual information. This novel approach gives a clustering solution analytically in a computationally efficient way via kernel eigenvalue decomposition. Furthermore, we provide a practical model selection procedure that allows us to objectively optimize tuning parameters included in the kernel function. Through experiments, we demonstrate the usefulness of the proposed approach.

Surface gravimetry on Dimorphos  

2021 ◽  
Author(s):  
Özgür Karatekin ◽  
Birgit Ritter ◽  
Jose Carrasco ◽  
Matthias Noeker ◽  
Ertan Umit ◽  
...  
Keyword(s):  
Landing Site ◽  
Temporal Variations ◽  
The Body ◽  
Current Status ◽  
Main Body ◽  
Gravity Vector ◽  
Centrifugal Forces ◽  
Soft Landing ◽  
Binary Asteroid ◽  
Operational Concept

<p>In the frame work of HERA mission, the gravimeter for small solar system objects (GRASS) has been developed to measure the local acceleration vector on the surface of the moonlet of the binary asteroid, Dimorphos. GRASS will be onboard Juventas CubeSat which is one of the two daughtercraft of ESA’s Hera spacecraft. Launched in 2024 it will arrive in the binary system in 2026. Following the soft-landing of the Juventas CubeSat, GRASS will record the temporal variation of the surface gravity vector.</p><p>The average gravitational force expected on the Dimorphos surface is around 5 x 10<sup>-5</sup> m s<sup>-2</sup> (or 5 mGal). Apart from the self-gravitation of the body, centrifugal forces and the acceleration due to the main body of the system contribute to the surface acceleration. The temporal variations of local gravity vector at the landing site will be used to constrain the geological substructure (mass anomalies, local depth and lateral variations of regolith) as well as the surface geophysical environment (tides, dynamic sloped and centrifugal forces).</p><p>We will present the GRASS science objectives in the Hera mission the operational concept that is foreseen to reach these objectives, its current status of development including first test results and the by simulation estimated performances of the instrument.</p><p> </p>

Trajectory Optimization of Hypersonic Vehicle Using Gauss Pseudospectral Method

2011 ◽  
Vol 110-116 ◽  
pp. 5232-5239 ◽  
Author(s):  
Rahman Tawfiqur ◽  
Hao Zhou ◽  
Yong Zhi Sheng ◽  
Ya Min Younis ◽  
Ke Nan Zhang
Keyword(s):  
Trajectory Optimization ◽  
Pseudospectral Method ◽  
High Accuracy ◽  
Hypersonic Vehicle ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Rocket Propulsion ◽  
Gauss Pseudospectral Method ◽  
Solid Rocket ◽  
Multi Phase

The paper presents a gauss pseudospectral solution for the trajectory optimization problem of a hypersonic vehicle. Determination of optimal trajectory of a hypersonic vehicle is of great interest due to the different path and boundary conditions that need to be met for high accuracy. Recent researches show that pseudospectral methods are capable of providing high accuracy in computationally efficient manner. The hypersonic vehicle optimized here is accelerated through solid rocket propulsion to mach 3.5 and after ejection of the rocket motor it is accelerated to mach 6 where it starts cruise for reaching target. The flight profile which is divided into boost, ascent, cruise and dive phase is optimized using multi-phase implementation programme of gauss pseudospectral method GPOPS. The optimization is carried out in 2D assuming non-rotating flat earth assumption and considering propulsion, dynamic and atmospheric constraints. The results are then analyzed for max range and max final velocity and hit angle. The results are found to be feasible.

Topographic and Geomorphological Mapping and Analysis of the Chang'E-4 Landing Site on the Far Side of the Moon

2020 ◽  
Vol 86 (4) ◽  
pp. 247-258 ◽  
Author(s):  
Bo Wu ◽  
Fei Li ◽  
Han Hu ◽  
Yang Zhao ◽  
Yiran Wang ◽  
...  
Keyword(s):  
Landing Site ◽  
Joint Analysis ◽  
The Sun ◽  
The Moon ◽  
Soft Landing ◽  
Geomorphological Mapping ◽  
Von Karman ◽  
Lunar Reconnaissance Orbiter ◽  
Integrated Processing ◽  
Von Kármán

The Chinese lunar probe Chang'E-4 successfully landed in the Von Kármán crater on the far side of the Moon. This paper presents the topographic and geomorphological mapping and their joint analysis for selecting the Chang'E-4 landing site in the Von Kármán crater. A digital topographic model (<small>DTM</small>) of the Von Kármán crater, with a spatial resolution of 30 m, was generated through the integrated processing of Chang'E-2 images (7 m/pixel) and Lunar Reconnaissance Orbiter (<small>LRO</small>) Laser Altimeter (<small>LOLA</small>) data. Slope maps were derived from the <small>DTM</small>. Terrain occlusions to both the Sun and the relay satellite were studied. Craters with diameters ≥ 70 m were detected to generate a crater density map. Rocks with diameters ≥ 2 m were also extracted to generate a rock abundance map using an <small>LRO</small> narrow angle camera (<small>NAC</small>) image mosaic. The joint topographic and geomorphological analysis identified three subregions for landing. One of them, recommended as the highest-priority landing site, was the one in which Chang'E-4 eventually landed. After the successful landing of Chang'E-4, we immediately determined the precise location of the lander by the integrated processing of orbiter, descent and ground images. We also conducted a detailed analysis around the landing location. The results revealed that the Chang'E-4 lander has excellent visibility to the Sun and relay satellite; the lander is on a slope of about 4.5° towards the southwest, and the rock abundance around the landing location is almost 0. The developed methods and results can benefit future soft-landing missions to the Moon and other celestial bodies.

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