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

D’Ambrosio, Andrea; Carbone, Andrea; Spiller, Dario; Curti, Fabio

doi:10.3390/aerospace8070195

Cited by 10 publications

(5 citation statements)

References 50 publications

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“…At this point, to obtain the solution, we need to only operate a measurement of the qubtis [34]. Equation (3) shows a generic transition between the Dirver and Problem Hamiltonian, where A(t/T) and B(t/T) are arbitrary functions such that A(0) = 1, B(0) = 0 and A(1) = 0, B(1) = 1. In Equation ( 4), the state of the system is shown by the eigenvalues equation at the start and at the end of the annealing process, where ψ 0,D stands for the ground state of the Driver Hamiltonian and ψ 0,P is the ground state of the Problem Hamiltonian, the solution of the optimization problem.…”

Section: Quantum Annealing Optimizationmentioning

confidence: 99%

“…In our procedure, we chose to minimize the number of variables, thus selecting l = 1, leading to polynomial degree n being simply equal to m. The decision to set l = 1 was primarily dictated by a desire to keep the approach simple and practical, making it easier to convert to the QUBO format. Moreover, it is worth noting that in other works, a similar choice was made to set "L" to 1 [3,36]. This approach is commonly adopted in trajectory optimization research for its practical advantages.…”

Section: Transcription Proceduresmentioning

confidence: 99%

“…Rughani et al in [2] utilized GA to optimize orbital trajectories for spacecraft swarms, facilitating collaborative tasks in space and collision avoidance. Particle Swarm Optimization demonstrated its prowess in [3], addressing real-time guidance for autonomous lunar landings and hazard avoidance, with promising experimental results. In [4], Particle Swarm Optimization was exploited to maximize asteroid surface coverage using hopping trajectories in lowgravity environments.…”

Section: Introductionmentioning

confidence: 99%

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Cutting-Edge Trajectory Optimization through Quantum Annealing

Carbone,

De Grossi,

Spiller

2023

Applied Sciences

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This paper introduces an innovative approach to explore the capabilities of Quantum Annealing (QA) for trajectory optimization in dynamic systems. The proposed method involves transforming trajectory optimization problems into equivalent binary optimization problems using Quadratic Unconstrained Binary Optimization (QUBO) representation. The procedure is general and adaptable, making it applicable to a wide range of optimal control problems that entail the satisfaction of dynamic, boundary, and path constraints. Specifically, the trajectory is discretized and approximated using polynomials. In contrast to the conventional approach of determining the polynomial degree (n) solely based on the number of boundary conditions, a specific factor is introduced in our method to augment the polynomial degree. As a result, the ultimate polynomial degree is calculated as a composite of two components: n = l + (m−1), where m denotes the count of boundary conditions and l signifies the number of independent variables. By leveraging inverse dynamics, the control required to follow the approximated trajectory can be determined as a linear function of independent variables l. As a result, the optimization function, which is represented by the integral of the square of the control, can be formulated as a QUBO problem and the QA is employed to find the optimal binary solutions.

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Section: Quantum Annealing Optimizationmentioning

confidence: 99%

Section: Transcription Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cutting-Edge Trajectory Optimization through Quantum Annealing

Carbone,

De Grossi,

Spiller

2023

Applied Sciences

Self Cite

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“…Hazard detection has typically been carried out using more traditional methods. Among these methods, there are the Canny [ 9 , 10 ] and Sobel [ 11 ] algorithms, which detect the edges in an image, and the various corner detection algorithms [ 12 , 13 ], which can provide good repeatability along with localization [ 14 ]. However, these methods may not meet the accuracy and computational efficiency requirements for real-time use on a spacecraft hazard detection system.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Ghilardi,

Furfaro

2023

Sensors

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Hazard detection is fundamental for a safe lunar landing. State-of-the-art autonomous lunar hazard detection relies on 2D image-based and 3D Lidar systems. The lunar south pole is challenging for vision-based methods. The low sun inclination and the terrain rich in topographic features create large areas in shadow, hiding the terrain features. The proposed method utilizes a vision transformer (ViT) model, which is a deep learning architecture based on the transformer blocks used in natural language processing, to solve this problem. Our goal is to train the ViT model to extract terrain features information from low-light RGB images. The results show good performances, especially at high altitudes, beating the UNet, one of the most popular convolutional neural networks, in every scenario.

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“…Liu [18] proposed a method for selecting a safe landing site by applying the optimization problem. In addition, methods for finding craters through machine learning and deep learning are being studied for hazard avoidance or real-time evaluation [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

et al. 2021

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In this paper, a method for predicting the landing stability of a lunar lander by a classification map of the landing stability is proposed, considering the soft soil characteristics and the slope angle of the lunar surface. First, the landing stability condition in terms of the safe (=stable), sliding (=unstable), and tip-over (=statically unstable) possibilities was checked by dropping a lunar lander onto flat lunar surfaces through finite-element (FE) simulation according to the slope angle, friction coefficient, and soft/rigid ground, while the vertical touchdown velocity was maintained at 3 m/s. All of the simulation results were classified by a classification map with the aid of logistic regression, a machine-learning classification algorithm. Finally, the landing stability status was efficiently predicted by Monte Carlo (MC) simulation by just referring to the classification map for 10,000 input datasets, consisting of the friction coefficient, slope angles, and rigid/soft ground. To demonstrate the performance, two virtual lunar surfaces were employed based on a 3D terrain map of the LRO mission. Then, the landing stability was validated through landing simulation of an FE model of a lunar lander requiring high computation cost. The prediction results showed excellent agreement with those of landing simulations with a negligible computational cost of around a few seconds.

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

Cited by 10 publications

References 50 publications

Cutting-Edge Trajectory Optimization through Quantum Annealing

Cutting-Edge Trajectory Optimization through Quantum Annealing

Image-Based Lunar Hazard Detection in Low Illumination Simulated Conditions via Vision Transformers

Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

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