Non-geometric mobility hazards such as rover slippage and sinkage posing great challenges to costly planetary missions are closely related to the mechanical properties of terrain. In-situ proprioceptive processes for rovers to estimate terrain mechanical properties need to experience different slip as well as sinkage and are helpless to untraversed regions. This paper proposes to predict terrain mechanical properties with vision in the distance, which expands the sensing range to the whole view and can partly halt potential slippage and sinkage hazards in the planning stage. A semantic-based method is designed to predict bearing and shearing properties of terrain in two stages connected with semantic clues. The former segmentation phase segments terrain with a light-weighted network promising to be applied onboard with competitive 93% accuracy and high recall rate over 96%, while the latter inference phase predicts terrain properties in a quantitative manner based on human-like inference principles. The prediction results in several test routes are 12.5% and 10.8% in full-scale error and help to plan appropriate strategies to avoid suffering non-geometric hazards.
Physical Reservoir Computing in Hook-Shaped Rover Wheel Spokes for Real-Time Terrain Identification
Dyson School of Design Engineering, Imperial College London（帝国理工学院伦敦校区设计工程学院）；Department of Computer Science, University of Aberdeen（阿伯丁大学计算机科学系）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）
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）
We present a light-weight body-terrain clearance evaluation algorithm for the automated path planning of NASA's Mars 2020 rover. Extraterrestrial path planning is challenging due to the combination of terrain roughness and severe limitation in computational resources. Path planning on cluttered and/or uneven terrains requires repeated safety checks on all the candidate paths at a small interval. Predicting the future rover state requires simulating the vehicle settling on the terrain, which involves an inverse-kinematics problem with iterative nonlinear optimization under geometric constraints…
Southern University of Science and Technology (SUSTech)（南方科技大学）
Robot mobility is critical for mission success, especially in soft or deformable terrains, where the complex wheel-soil interaction mechanics often leads to excessive wheel slip and sinkage, causing the eventual mission failure. To improve the success rate, online mobility prediction using vision, infrared imaging, or model-based stochastic methods have been used in the literature. This paper proposes an on-board mobility prediction approach using an articulated wheeled bevameter that consists of a force-controlled arm and an instrumented bevameter (with force and vision sensors) as its end-ef…
Understanding the wheel-terrain interaction is of great importance to improve the maneuverability and traversability of the rovers. A well-developed sensing device carried by the rover would greatly facilitate the complex risk-reducing operations on sandy terrains. In this paper, an instrumented wheel-on-limb (WOL) system of planetary rovers for wheel-terrain interaction characterization is presented. Assuming the function of a passive suspension of the wheel, the WOL system allows itself to follow the terrain contour, and keep the wheel remain lowered onto the ground during rover motion inclu…
Keio University（庆应义塾大学）；OMRON SINIC X Corporation（欧姆龙SINIC X公司）
Machine learning (ML) plays a crucial role in assessing traversability for autonomous rover operations on deformable terrains but suffers from inevitable prediction errors. Especially for heterogeneous terrains where the geological features vary from place to place, erroneous traversability prediction can become more apparent, increasing the risk of unrecoverable rover's wheel slip and immobilization. In this work, we propose a new path planning algorithm that explicitly accounts for such erroneous prediction. The key idea is the probabilistic fusion of distinctive ML models for terrain type c…
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）
West Virginia University（西弗吉尼亚大学）；The Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）
This paper compares the performance of adaptive and robust Kalman filter algorithms in improving wheel-inertial odometry on low featured rough terrain. Approaches include classical adaptive and robust methods as well as variational methods, which are evaluated experimentally on a wheeled rover in terrain similar to what would be encountered in planetary exploration. Variational filters show improved solution accuracy compared to the classical adaptive filters and are able to handle erroneous wheel odometry measurements and keep good localization for longer distances without significant drift. …
Advance Simulation Method for Wheel-Terrain Interactions of Space Rovers: A Case Study on the UAE Rashid Rover
Khalifa University Center for Autonomous and Robotic Systems（哈利法大学自主与机器人系统中心）；Advanced Research and Innovation Center（先进研究与创新中心）；Khalifa University（哈利法大学）
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）
Slip detection is of fundamental importance for the safety and efficiency of rovers driving on the surface of extraterrestrial bodies. Current planetary rover slip detection systems rely on visual perception on the assumption that sufficient visual features can be acquired in the environment. However, visual-based methods are prone to suffer in perceptually degraded planetary environments with dominant low terrain features such as regolith, glacial terrain, salt-evaporites, and poor lighting conditions such as dark caves and permanently shadowed regions. Relying only on visual sensors for slip…
Polytechnic of Bari（巴里理工大学）；University of Salento（萨伦托大学）；National Research Council（国家研究委员会）；Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing（智能工业技术与先进制造系统研究所）
There is a growing interest in new sensing technologies and processing algorithms to increase the level of driving automation towards self-driving vehicles. The challenge for autonomy is especially difficult for the negotiation of uncharted scenarios, including natural terrain. This paper proposes a method for terrain unevenness estimation that is based on the power spectral density (PSD) of the surface profile as measured by exteroceptive sensing, that is, by using a common onboard range sensor such as a stereoscopic camera. Using these components, the proposed estimator can evaluate terrain …
HE Space for ESA – European Space Agency（HE Space 公司（为欧洲空间局））；European Space Agency（欧洲空间局）；University of Malaga（马拉加大学）
This paper proposes a Guidance, Navigation, and Control (GNC) architecture for planetary rovers targeting the conditions of upcoming Mars exploration missions such as Mars 2020 and the Sample Fetching Rover (SFR). The navigation requirements of these missions demand a control architecture featuring autonomous capabilities to achieve a fast and long traverse. The proposed solution presents a two-level architecture where the efficient navigation (low) level is always active and the full navigation (upper) level is enabled according to the difficulty of the terrain. The first level is an efficien…
The zero-velocity update (ZUPT) algorithm provides valuable state information to maintain the inertial navigation system (INS) reliability when stationary conditions are satisfied. Employing ZUPT along with leveraging non-holonomic constraints can greatly benefit wheeled mobile robot dead-reckoning localization accuracy. However, determining how often they should be employed requires consideration to balance localization accuracy and traversal rate for planetary rovers. To address this, we investigate when to autonomously initiate stops to improve wheel-inertial odometry (WIO) localization per…
Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Center for Autonomy, University of Texas at Austin（自主性中心，德克萨斯大学奥斯汀分校）；Space Systems Laboratory, University of Maryland（空间系统实验室，马里兰大学）
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）
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Manipal Institute of Technology（马尼帕尔理工学院）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars. MLNav makes judicious use of machine learning to enhance the efficiency of path planning while fully respecting safety constraints. In particular, the dominant computational cost in such safety-critical settings is running a model-based safety checker on the proposed paths. Our learned search heuristic can simultaneously predict the feasibility for all path options in a single run, and the model-based safety checker…
Terrain assessment is a key aspect for autonomous exploration rovers, surrounding environment recognition is required for multiple purposes, such as optimal trajectory planning and autonomous target identification. In this work we present a technique to generate accurate three-dimensional semantic maps for Martian environment. The algorithm uses as input a stereo image acquired by a camera mounted on a rover. Firstly, images are labeled with DeepLabv3+, which is an encoder-decoder Convolutional Neural Networl (CNN). Then, the labels obtained by the semantic segmentation are combined to stereo …
We present an approach to enhance wheeled planetary rover dead-reckoning localization performance by leveraging the use of zero-type constraint equations in the navigation filter. Without external aiding, inertial navigation solutions inherently exhibit cubic error growth. Furthermore, for planetary rovers that are traversing diverse types of terrain, wheel odometry is often unreliable for use in localization, due to wheel slippage. For current Mars rovers, computer vision-based approaches are generally used whenever there is a high possibility of positioning error; however, these strategies r…
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）
Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Mississippi State University（密西西比州立大学）
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a sem…
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）
UWB Anchor Based Localization of a Planetary Rover
Julius Maximilian University Würzburg（维尔zburg尤利乌斯-马克西米利安大学）；ETH Zürich（苏黎世联邦理工学院）；Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学）；Austrian Space Forum（奥地利空间论坛）
An adaptive hierarchical control framework for quadrupedal robots in planetary exploration
Robotics Innovation Center, DFKI GmbH（德意志联邦共和国家际机器人创新中心，DFKI GmbH）；German Aerospace Center, Institute of Space Systems（德国航空航天中心，空间系统研究所）；University of Bremen（不莱梅大学）
Quadrupeds for Planetary Exploration: Field Testing Control Algorithms on an Active Volcano
Robotics Innovation Center, DFKI GmbH（机器人创新中心，DFKI GmbH）；German Aerospace Center, Institute of Space Systems（德国航空航天中心，空间系统研究所）；University of Bremen（不莱梅大学）
Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots
ETH Zurich（苏黎世联邦理工学院）；Harbin Institute of Technology（哈尔滨工业大学）；University of Bern（伯尔尼大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Lucerne University of Applied Sciences and Art（卢塞恩应用科学与艺术大学）
Legged Autonomous Surface Science In Analogue Environments (LASSIE): Making Every Robotic Step Count in Planetary Exploration
Collaborative Robotics and Intelligent Systems Institute, Oregon State University（协作机器人与智能系统研究所，俄勒冈州立大学）；Department of Geology and Geophysics, Texas A&M University（地质与地球物理系，德克萨斯大学）；Department of Electrical and Computer Engineering, University of Southern California（电气与计算机工程系，南加州大学）；Department of Earth and Environmental Science, University of Pennsylvania（地球与环境科学系，宾夕法尼亚大学）；Department of Astronomy and Planetary Science, Northern Arizona University（天文学与行星科学系，北方亚利桑那大学）；School of Earth and Atmospheric Sciences, Georgia Institute of Technology（地球与大气科学学院，佐治亚理工学院）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔航天飞行中心）；NASA Johnson Space Center（美国国家航空航天局约翰逊航天中心）；Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Psychology, Temple University（心理学系， Temple 大学）
German Aerospace Center (DLR)（德国航空航天中心）；University of Technology Sydney（悉尼科技大学）；Technical University of Munich (TUM)（慕尼黑工业大学）
The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point c…
Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning
Department of Computer Sciences and Artificial Intelligence, University of Alicante（计算机科学与人工智能系，阿利坎特大学）；Department of Physics, Systems Engineering, and Signal Theory, University of Alicante（物理、系统工程与信号理论系，阿利坎特大学）；Department of Systems and Automatics Engineering, University of Málaga（系统与自动工程系，马拉加大学）
