Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach
Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field...
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| Veröffentlicht in: | Acta astronautica Jg. 171; S. 156 - 171 |
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| Abstract | Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints.
•A Deep Reinforcement Learning (DRL) framework for structured planetary landing guidance is presented.•The DRL framework is based on a customized actor-critic approach that employs Extreme Learning Machines (ELM).•The algorithm learns the ZEM/ZEV generalized feedback guidance gains as function of the spacecraft state.•The gains are adapted to ensure both quasi-optimality and flight-constraints.•Stability analysis shows the proposed approach is globally stable. |
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| AbstractList | Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints. Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm, although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the above-mentioned guidance architecture according to the given problem constraints. •A Deep Reinforcement Learning (DRL) framework for structured planetary landing guidance is presented.•The DRL framework is based on a customized actor-critic approach that employs Extreme Learning Machines (ELM).•The algorithm learns the ZEM/ZEV generalized feedback guidance gains as function of the spacecraft state.•The gains are adapted to ensure both quasi-optimality and flight-constraints.•Stability analysis shows the proposed approach is globally stable. |
| Author | Linares, Richard Scorsoglio, Andrea Massari, Mauro Furfaro, Roberto |
| Author_xml | – sequence: 1 givenname: Roberto orcidid: 0000-0001-6076-8992 surname: Furfaro fullname: Furfaro, Roberto email: robertof@email.arizona.edu organization: Department of Systems & Industrial Engineering, Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ, 85721, USA – sequence: 2 givenname: Andrea orcidid: 0000-0001-5875-3804 surname: Scorsoglio fullname: Scorsoglio, Andrea email: andreascorsoglio@email.arizona.edu organization: Department of Systems & Industrial Engineering, University of Arizona, Tucson, AZ, 85721, USA – sequence: 3 givenname: Richard surname: Linares fullname: Linares, Richard email: linaresr@mit.edu organization: Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA – sequence: 4 givenname: Mauro orcidid: 0000-0003-4535-8543 surname: Massari fullname: Massari, Mauro email: mauro.massari@polimi.it organization: Department of Aerospace Science and Technology, Politecnico di Milano, Milan, 20156, ITA, Italy |
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| SubjectTerms | Adaptive algorithms Algorithms Closed loops Closed-loop guidance Deep reinfocement learning Feedback Machine learning Optimal landing guidance Planetary landing Solar system Spacecraft Spacecraft guidance |
| Title | Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach |
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