Trajectory optimization for lunar rover performing vertical takeoff vertical landing maneuvers in the presence of terrain

Ma, Lin; Wang, Kexin; Xu, Zuhua; Shao, Zili; Song, Zhengyu; Biegler, Lorenz T.

doi:10.1016/j.actaastro.2018.03.013

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…By setting homotopy parameters and paths, a series of homotopy optimization sub-problems are solved in turn starting from an easy-to-solve problem, where the optimal solution of the current sub-problem is taken as the initial value of the next sub-problem until the solution to the original problem is obtained (Wang C and Song, 2018b). For the optimal trajectory design of VTVL transfer on the lunar surface, Ma et al (2018a) proposed a simultaneous approach and an adaptive discrete mesh refinement algorithm based on the orthogonal collocation on finite element (OCFE). Although terrain obstacle avoidance constraints in this case complicate the problem, the homotopy backtracking strategy enhances the solvability of the problem.…”

Section: Other Numerical Optimization-based Online Planning 1 Homotopy Methodsmentioning

confidence: 99%

“…Although it is still challenging to prove the convergence of many direct methods for NLP problems, they are effective ways to analyze planetary landing features and have developed in many directions, such as obstacle avoidance (Ma et al, 2016;Zhang B et al, 2016;Wang C and Song, 2018b), vertical takeoff and vertical landing (VTVL) simultaneous optimization (Ma et al, 2018a), and pseudospectral discrete convex optimization (Sagliano and Mooij, 2018;Sagliano, 2018aSagliano, , 2018bWenzel et al, 2018;Malyuta et al, 2019). Simulations have shown that a reasonable initial value can help quickly find the (local) optimal solution of an NLP problem, and the computing speed and convergence effect are not inferior to the counterparts of the convex optimization methods.…”

Section: Introductionmentioning

confidence: 99%

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Survey of autonomous guidance methods for powered planetary landing

Song

Theil

Seelbinder

et al. 2020

Front Inform Technol Electron Eng

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This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three-and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

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Section: Other Numerical Optimization-based Online Planning 1 Homotopy Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survey of autonomous guidance methods for powered planetary landing

Song

Theil

Seelbinder

et al. 2020

Front Inform Technol Electron Eng

View full text Add to dashboard Cite

show abstract

“…Ma et al [ 23 ] have suggested a trajectory optimization system for lunar rovers that performs VTVL using variable thrust propulsion in terrain. (i) A TO problem of VTVL including 3-D kinematics with dynamics mode, boundary stages, and trajectory constraints was designed.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission

Latif

Mehedi

Iskanderani

et al. 2022

Computational Intelligence and Neuroscience

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This manuscript proposes a hybrid method for landing trajectory generation of unmanned lunar mission. The proposed hybrid control scheme is the joint execution of the human urbanization algorithm (HUA) and political optimizer (PO) with radial basis functional neural network (RBFNN); hence it is named as HUA-PORFNN method. The HUA is a metaheuristic method, and it is used to solve several optimization issues and several nature-inspired methods to enhance the convergence speed with quality. On the other hand, multiple-phased political processes inspire the PO. The work aims to guide the lander with minimal fuel consumption from the initial to the final stage, thus minimizing the lunar soft landing issues based on the given cost of operation. Here, the HUAPO method is implemented to overcome thrust discontinuities, checkpoint constraints are suggested for connecting multi-landing phases, angular attitude rate is modeled to obtain radical change rid, and safeguards are enforced to deflect collision along with obstacles. Moreover, first, the issues have been resolved according to the proposed HUAPO method. Here, energy trajectories with 3 terminal processes are deemed. Additionally, the proposed HUAPO method is executed on MATLAB/Simulink site, and the performance of the proposed method is compared with other methods.

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“…The general idea of the homotopy technique is to solve a series of simple transition problems so that the solution of the transition problem gradually approaches the optimal solution of the primal problem. This method reduces the problem's difficulty, thereby significantly improving the success rate of trajectory planning [4,[23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization with Complex Obstacle Avoidance Constraints via Homotopy Network Sequential Convex Programming

Cheng

et al. 2022

Aerospace

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Space vehicles’ real-time trajectory optimization is the key to future automatic guidance. Still, the current sequential convex programming (SCP) method suffers from a low convergence rate and poor real-time performance when dealing with complex obstacle avoidance constraints (OACs). Given the above challenges, this work combines homotopy and neural network techniques with SCP to propose an innovative algorithm. Firstly, a neural network was used to fit the minimum signed distance field at obstacles’ different “growth” states to represent the OACs. Then, the network was embedded with the SCP framework, thus smoothly transforming the OACs from simple to complex. Numerical simulations showed that the proposed algorithm can efficiently deal with trajectory optimization under complex OACs such as a “maze”, and the algorithm has a high convergence rate and flexible extensibility.

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Trajectory optimization for lunar rover performing vertical takeoff vertical landing maneuvers in the presence of terrain

Cited by 17 publications

References 28 publications

Survey of autonomous guidance methods for powered planetary landing

Survey of autonomous guidance methods for powered planetary landing

Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission

Trajectory Optimization with Complex Obstacle Avoidance Constraints via Homotopy Network Sequential Convex Programming

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