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      Robust Entry Vehicle Guidance with Sampling-Based Invariant Funnels

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          Abstract

          Managing uncertainty is a fundamental and critical issue in spacecraft entry guidance. This paper presents a novel approach for uncertainty propagation during entry, descent and landing that relies on a new sum-of-squares robust verification technique. Unlike risk-based and probabilistic approaches, our technique does not rely on any probabilistic assumptions. It uses a set-based description to bound uncertainties and disturbances like vehicle and atmospheric parameters and winds. The approach leverages a recently developed sampling-based version of sum-of-squares programming to compute regions of finite time invariance, commonly referred to as "invariant funnels". We apply this approach to a three-degree-of-freedom entry vehicle model and test it using a Mars Science Laboratory reference trajectory. We compute tight approximations of robust invariant funnels that are guaranteed to reach a goal region with increased landing accuracy while respecting realistic thermal constraints.

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          Author and article information

          Journal
          04 November 2020
          Article
          2011.02441

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          submitted to IEEE Aerospace Conference (AeroConf2021), Big Sky, MT
          cs.RO

          Robotics

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