Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization

Blackmore, Lars; Açıkmeşe, Behçet; Scharf, Daniel P.

doi:10.2514/1.47202

Cited by 308 publications

(132 citation statements)

References 24 publications

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“…Typical examples include the promising application of convex optimization in the Mars precise landing problems with nonconvex constraints on the thrust magnitude [10][11][12][13][14][15]. Convex optimization has also found applications in rapidly designing descent trajectories for asteroid landing [16,17].…”

Section: Applications In Spacecraftmentioning

confidence: 99%

Survey of convex optimization for aerospace applications

2017

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Convex optimization is a class of mathematical programming problems with polynomial complexity for which state-of-the-art, highly efficient numerical algorithms with predeterminable computational bounds exist. Computational efficiency and tractability in aerospace engineering, especially in guidance, navigation, and control (GN&C), are of paramount importance. With theoretical guarantees on solutions and computational efficiency, convex optimization lends itself as a very appealing tool. Coinciding the strong drive toward autonomous operations of aerospace vehicles, convex optimization has seen rapidly increasing utility in solving aerospace GN&C problems with the potential for onboard real-time applications. This paper attempts to provide an overview on the problems to date in aerospace guidance, path planning, and control where convex optimization has been applied. Various convexification techniques are reviewed that have been used to convexify the originally nonconvex aerospace problems. Discussions on how to ensure the validity of the convexification process are provided. Some related implementation issues will be introduced as well.

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Section: Applications In Spacecraftmentioning

confidence: 99%

Survey of convex optimization for aerospace applications

2017

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“…The common objective of planetary landing guidance algorithms of previous studies is to precisely land on the predetermined landing site with minimum fuel consumption [1][2][3][4][5]. This implies that those algorithms are not suitable to figure out the maximum downrange of the lunar lander under fuel limits.…”

Section: Introductionmentioning

confidence: 99%

Lunar Lander Landing Site Decision in Low-Fuel Case

Seo

Hong

Tahk

2016

MATEC Web of Conferences

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Abstract. This paper deals with lunar landing site decision algorithms for the case when the lunar lander is not able to reach the original landing site. A new landing site which minimizes the fuel consumption of the lunar rover is determined from landing site candidates located within the maximum reachable downrange of the lunar lander. A path planning algorithm is introduced to determine the new landing site which has the shortest path to the original landing site among the candidates. Numerical simulations are conducted to verify the performance of the proposed algorithm.

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“…In particular, [17] presents an approach for generating stand-alone C code for an SOCP solver that can run very efficiently and with low memory footprint. The use of such real-time SOCP solvers has already been considered for tasks such as landing of spacecraft (e.g., for NASA's Mars exploration project) [12].…”

Section: Lyapunov Theory and Optimizationmentioning

confidence: 99%

Some applications of polynomial optimization in operations research and real-time decision making

Ahmadi

Majumdar²

2015

Optim Lett

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We demonstrate applications of algebraic techniques that optimize and certify polynomial inequalities to problems of interest in the operations research and transportation engineering communities. Three problems are considered: (i) wireless coverage of targeted geographical regions with guaranteed signal quality and minimum transmission power, (ii) computing real-time certificates of collision avoidance for a simple model of an unmanned vehicle (UV) navigating through a cluttered environment, and (iii) designing a nonlinear hovering controller for a quadrotor UV, which has recently been used for load transportation. On our smaller-scale applications, we apply the sum of squares (SOS) relaxation and solve the underlying problems with semidefinite programming. On the larger-scale or real-time applications, we use our recently introduced "SDSOS Optimization" techniques which result in second order cone programs. To the best of our knowledge, this is the first study of real-time applications of sum of squares techniques in optimization and control. No knowledge in dynamics and control is assumed from the reader.

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Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization

Cited by 308 publications

References 24 publications

Survey of convex optimization for aerospace applications

Survey of convex optimization for aerospace applications

Lunar Lander Landing Site Decision in Low-Fuel Case

Some applications of polynomial optimization in operations research and real-time decision making

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