paper-with-me

홈 › Papers

Robust Fuel-Optimal Landing Guidance for Hazardous Terrain using Multiple Sliding Surfaces

2024-03-19 · Sheikh Zeeshan Basar, Satadal Ghosh

In any spacecraft landing mission, fuel-efficient precision soft landing while avoiding nearby hazardous terrain is of utmost importance. Very few existing literature have attempted addressing both the problems of precision soft landing and terrain avoidance simultaneously. To this end, an optimal terrain avoidance landing guidance (OTALG) was recently developed, which showed promising performance in avoiding the terrain while consuming near-minimum fuel. However, its performance significantly degrades in the face of external disturbances, indicating lack of robustness. To mitigate this problem, in this paper, a near fuel-optimal guidance law is developed to avoid terrain and achieve precision soft landing at the desired landing site. Expanding the OTALG formulation using sliding mode control with multiple sliding surfaces (MSS), the presented guidance law, named `MSS-OTALG', improves precision soft landing accuracy. Further, the sliding parameter is designed to allow the lander to avoid terrain by leaving the trajectory enforced by the sliding mode and eventually returning to it when the terrain avoidance phase is completed. And finally, the robustness of the MSS-OTALG is established by proving practical fixed-time stability. Extensive numerical simulations are also presented to showcase its performance in terms of terrain avoidance, low fuel consumption, and accuracy of precision soft landing under bounded atmospheric perturbations, thrust deviations, and constraints. Comparative studies against existing relevant literature validate a balanced trade-off of all these performance measures achieved by the developed MSS-OTALG.

📄 PDF Abstract BibTeX arXiv:2403.12584

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Fuel-Optimal Powered Descent Guidance for Hazardous Terrain

2023-11-09 · Sheikh Zeeshan Basar, Satadal Ghosh

Future interplanetary missions will carry more and more sensitive equipment critical for setting up bases for crewed missions. The ability to manoeuvre around hazardous terrain thus becomes a critical mission aspect. How…

Real-Time Stochastic Terrain Mapping and Processing for Autonomous Safe Landing

2024-09-14 · Kento Tomita, Koki Ho

Onboard terrain sensing and mapping for safe planetary landings often suffer from missed hazardous features, e.g., small rocks, due to the large observational range and the limited resolution of the obtained terrain data…

Uncertainty Quantification

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

2021-02-24 · Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia, Tatsuwaki Nakagawa 외

This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone selection utilize local terrain features …

Deep Reinforcement LearningReinforcement Learning (RL)

You Only Crash Once: Improved Object Detection for Real-Time, Sim-to-Real Hazardous Terrain Detection and Classification for Autonomous Planetary Landings

2023-03-08 · Timothy Chase Jr, Chris Gnam, John Crassidis, Karthik Dantu

The detection of hazardous terrain during the planetary landing of spacecraft plays a critical role in assuring vehicle safety and mission success. A cheap and effective way of detecting hazardous terrain is through the …

Autonomous DrivingDeep LearningDomain Adaptationobject-detection+4

Model Predictive Guidance for Fuel-Optimal Landing of Reusable Launch Vehicles

2024-05-02 · Ki-Wook Jung, Sang-Don Lee, Cheol-Goo Jung, Chang-Hun Lee

This paper introduces a landing guidance strategy for reusable launch vehicles (RLVs) using a model predictive approach based on sequential convex programming (SCP). The proposed approach devises two distinct optimal con…