Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；Manipal Institute of Technology（曼帕尔工程学院）；Rakuten Inc.（乐天株式会社）；Computing and Mathematical Sciences（计算与数学科学学院）
Neil Abcouwer, Shreyansh Daftry, Siddarth Venkatraman, Tyler del Sesto, Olivier Toupet, Ravi Lanka, Jialin Song, Yisong Yue, Masahiro Ono
备注：
9 pages, 5 figures, Pre-Print, This work has been submitted to the IEEE for possible publication
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe. ACE is crucial for maintaining the safety of the rover, but is computationally expensive. If the most promising candidates in the list of paths are all found to be infeasible, ENav must continue to search the list and run time-consuming ACE evaluations until a feasible path is found. In this paper, we present two heuristics that, given a terrain heightmap around the rover, produce cost estimates that more effectively rank the candidate paths before ACE evaluation. The first heuristic uses Sobel operators and convolution to incorporate the cost of traversing high-gradient terrain. The second heuristic uses a machine learning (ML) model to predict areas that will be deemed untraversable by ACE. We used physics simulations to collect training data for the ML model and to run Monte Carlo trials to quantify navigation performance across a variety of terrains with various slopes and rock distributions. Compared to ENav's baseline performance, integrating the heuristics can lead to a significant reduction in ACE evaluations and average computation time per planning cycle, increase path efficiency, and maintain or improve the rate of successful traverses. This strategy of targeting specific bottlenecks with ML while maintaining the original ACE safety checks provides an example of how ML can be infused into planetary science missions and other safety-critical software.
Fast Approximate Clearance Evaluation for Rovers with Articulated Suspension Systems
Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；Ècole Normale Supèrieure de Paris（巴黎高等师范学院）；Indian Institute of Technology（印度理工学院）
Towards An Adaptive Locomotion Strategy For Quadruped Rovers: Quantifying When To Slide Or Walk On Planetary Slopes
Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia (IIT)（动态腿系统实验室，意大利技术研究院）；Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università di Catania（电气电子与信息工程系，卡塔尼亚大学）
Field Assessment of Force Torque Sensors for Planetary Rover Navigation
French Space Agency（法国空间局）；European Space Agency（欧洲航天局）；European Union Agency for the Space Programme（欧洲联盟空间计划机构）；National Aeronautics and Space Administration（美国国家航空航天局）
PRISM: Multimodal Terrain Mapping for Rover Navigation in Unstructured Environments
SnT, University of Luxembourg（卢森堡大学安全、可靠性和信任跨学科中心）；Department of Automation and Systems Engineering, Universidad de Málaga, Andalucía Tech（马拉加大学自动化与系统工程系，安达卢西亚技术大学）
BEVIO: Efficient Bird's-Eye-View based Sparse-Update Visual-Inertial Odometry for Lunar Day-Night Navigation
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Autonomous Robots Lab at the Norwegian University of Science and Technology（挪威科学技术大学自主机器人实验室）
SlipNet: Enhancing Slip Cost Mapping for Autonomous Navigation on Heterogeneous and Deformable Terrains
Khalifa University Center for Autonomous and Robotic Systems (KUCARS)（哈利法大学自主与机器人系统中心）；Advanced Research and Innovation Center (ARIC)（先进研究与创新中心）；Khalifa University（哈利法大学）
Learning to Drive on Mars: Visual Multimodal Traversability Estimation for Off-World Navigation
University of Southern California（南加州大学）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Washington State University（华盛顿州立大学）
VL-Explore: Zero-shot Vision-Language Exploration and Target Discovery by Mobile Robots
RoboPI Laboratory, Dept. of ECE, University of Florida (UF)（罗波实验室，电子工程系，佛罗里达大学）；FOCUS Laboratory, Dept. of ECE, University of Florida (UF)（焦点实验室，电子工程系，佛罗里达大学）；Amazon Robotics（亚马逊机器人技术）
本文提出 VL-Explore 导航流程，利用 CLIP 和单目视觉在未知环境中实现零样本同步探索与目标发现，并通过 Open Rover 实车验证其优于传统地图遍历且接近依赖先验知识的方法。
