Bio-inspired Autonomous Robot with a Novel Mobility Mechanism for Planetary Cave and Lava Tubes Exploration

2022 ◽  
Author(s):  
Juliana Barstow ◽  
Celeste E. Flores ◽  
Masoud Naghdi ◽  
Mostafa Hassanalian
Keyword(s):  
Autonomous Robot ◽  
Lava Tubes

scholarly journals A potential subsurface cavity in the continuous ejecta deposits of the Ziwei crater discovered by the Chang’E-3 mission

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Chunyu Ding ◽  
Zhiyong Xiao ◽  
Yan Su
Keyword(s):  
Lunar Regolith ◽  
Lava Tubes ◽  
Impact Ejecta ◽  
Comprehensive Comparison ◽  
Ground Penetrating ◽  
Shallow Crust ◽  
Low Permittivity ◽  
Mare Basalts ◽  
Subsurface Cavities ◽  
High Bulk

AbstractIn the radargram obtained by the high-frequency lunar penetrating radar onboard the Chang’E-3 mission, we notice a potential subsurface cavity that has a smaller permittivity compared to the surrounding materials. The two-way travel time between the top and bottom boundaries of the potential cavity is ~ 21 ns, and the entire zone is located within the continuous ejecta deposits of the Ziwei crater, which generally have similar physical properties to typical lunar regolith. We carried out numerical simulations for electromagnetic wave propagation to investigate the nature of this low-permittivity zone. Assuming different shapes for this zone, a comprehensive comparison between our model results and the observed radargram suggests that the roof of this zone is convex and slightly inclined to the south. Modeling subsurface materials with different relative permittivities suggests that the low-permittivity zone is most likely formed due to a subsurface cavity. The maximum vertical dimension of this potential cavity is ~ 3.1 m. While the continuous ejecta deposits of Ziwei crater are largely composed of pre-impact regolith, competent mare basalts were also excavated, which is evident by the abundant meter-scale boulders on the wall and rim of Ziwei crater. We infer that the subsurface cavity is supported by excavated large boulders, which were stacked during the energetic emplacement of the continuous ejecta deposits. However, the exact geometry of this cavity (e.g., the width) cannot be constrained using the single two-dimensional radar profile. This discovery indicates that large voids formed during the emplacement of impact ejecta should be abundant on the Moon, which contributes to the high bulk porosity of the lunar shallow crust, as discovered by the GRAIL mission. Our results further suggest that ground penetrating radar is capable of detecting and deciphering subsurface cavities such as lava tubes, which can be applied in future lunar and deep space explorations.

Lunar Pits and Lava Tubes for a Modern Ark

2021 ◽  
Author(s):  
Alvaro Diaz-Flores ◽  
Claire Pedersen ◽  
Yinan Xu ◽  
Lindsey Williams ◽  
Cho Lik Chan ◽  
...  
Keyword(s):  
Lava Tubes

scholarly journals EfficientPS: Efficient Panoptic Segmentation

2021 ◽  
Author(s):  
Rohit Mohan ◽  
Abhinav Valada
Keyword(s):  
State Of The Art ◽  
Autonomous Robot ◽  
Multi Scale ◽  
Contextual Features ◽  
New Variant ◽  
Competent Functioning ◽  
Segmentation Task

AbstractUnderstanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

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