16 citations · 57 across the 9 of their papers we have counts for
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cs.RO2023★ 2 cited
Stochastic Hazard Detection For Landing Under Topographic Uncertainty
Kento Tomita, Koki Ho
Autonomous hazard detection and avoidance is a key technology for future landing missions in unknown surface conditions. Current state-of-the-art stochastic algorithms assume simpl…
cs.RO2021★ 16 cited
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers
Keidai Iiyama, Kento Tomita, Bhavi A. Jagatia +2
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 s…
cs.RO2021★ 13 cited
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
Kento Tomita, Katherine A. Skinner, Koki Ho
Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the…