China’s Mars rover, Zhurong, touched down on Utopia Planitia in the northern lowlands of Mars (109.925° E, 25.066° N) in May 2021, and has been conducting in situ investigations of the landing area in conjunction with the Tianwen-1 orbiter. Here we present surface properties derived from the Zhurong rover’s traverse during the first 60 sols of rover operations. Our analysis of the rover’s position from locomotion data and camera imagery over that time shows that the rover traversed 450.9 m southwards over a flat surface with mild wheel slippage. Soil parameters determined by terramechanics, which observes wheel–terrain interactions, indicate that the topsoil has high bearing strength and cohesion. The soil’s equivalent stiffness is estimated to range from 1,390 to 5,872 kPa per mN, and the internal friction angle ranges from 21° to 34° under a cohesion of 1.5 to 6 kPa. Aeolian bedforms in the area are primarily transverse aeolian ridges, indicating northeastern local wind directions. Surface rocks imaged by the rover cameras show evidence of physical weathering processes, such as wind erosion, and potential chemical weathering processes. Joint investigations utilizing the scientific payloads of the rover and the orbiter can provide insights into local aeolian and aqueous history, and the habitability evolution of the northern lowlands on Mars. Analysis of interactions between the wheels of the Zhurong rover and the terrain along the rover’s traverse reveals soils with high bearing strength and cohesion. 

/pdf/surface-characteristics-of-the-zhurong-mars-rover-traverse-2tg2xv7m.pdf

Surface characteristics of the Zhurong Mars rover traverse at Utopia Planitia

The interest in exploring planetary bodies for scientific investigation and in situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots’ inability to traverse steep slopes, unstructured terrain, and loose soil. In addition, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and postmission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25°, loose soil, and unstructured terrain, highlighting their advantages compared with wheeled rover systems. We successfully verified the approach in analog deployments at the Beyond Gravity ExoMars rover test bed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach. Description A team of legged robots with task-level autonomy was designed and field-tested for planetary analog exploration.

Scientific exploration of challenging planetary analog environments with a team of legged robots

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

WayFAST: Navigation With Predictive Traversability in the Field

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods. 

/pdf/wayfast-navigation-with-predictive-traversability-in-the-2ye0ar0u.pdf

—Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term naviga- tion. The place recognition community has made astonishing progress over the last 20 years, and this has attracted widespread research interest and application in multiple ﬁelds such as computer vision and robotics. However, few methods have shown promising place recognition performance in complex real-world scenarios, where long-term and large-scale appearance changes usually result in failures. Additionally, there is a lack of an integrated framework amongst the state-of-the-art methods that can handle all of the challenges in place recognition, which include appearance changes, viewpoint differences, robustness to unknown areas, and efﬁciency in real-world applications. In this work, we survey the state-of-the-art methods that target long- term localization and discuss future directions and opportunities. We start by investigating the formulation of place recognition in long-term autonomy and the major challenges in real-world environments. We then review the recent works in place recognition for different sensor modalities and current strategies for dealing with various place recognition challenges. Finally, we review the existing datasets for long-term localization and introduce our datasets and evaluation API for different approaches. This paper can be a tutorial for researchers new to the place recognition community and those who care about long-term robotics autonomy. We also provide our opinion on the frequently asked question in robotics: Do robots need accurate localization for long-term autonomy? A summary of this work, as well as our datasets and evaluation API are publicly available to the robotics community at: https://github.com/MetaSLAM/GPRS.

General Place Recognition Survey: Towards the Real-world Autonomy Age

The lunar nearside has been investigated by many uncrewed and crewed missions, but the farside of the Moon remains poorly known. Lunar farside exploration is challenging because maneuvering rovers with efficient locomotion in harsh extraterrestrial environment is necessary to explore geological characteristics of scientific interest. Chang’E-4 mission successfully targeted the Moon’s farside and deployed a teleoperated rover (Yutu-2) to explore inside the Von Kármán crater, conveying rich information regarding regolith, craters, and rocks. Here, we report mobile exploration on the lunar farside with Yutu-2 over the initial 2 years. During its journey, Yutu-2 has experienced varying degrees of mild slip and skid, indicating that the terrain is relatively flat at large scales but scattered with local gentle slopes. Cloddy soil sticking on its wheels implies a greater cohesion of the lunar soil than encountered at other lunar landing sites. Further identification results indicate that the regolith resembles dry sand and sandy loam on Earth in bearing properties, demonstrating greater bearing strength than that identified during the Apollo missions. In sharp contrast to the sparsity of rocks along the traverse route, small fresh craters with unilateral moldable ejecta are abundant, and some of them contain high-reflectance materials at the bottom, suggestive of secondary impact events. These findings hint at notable differences in the surface geology between the lunar farside and nearside. Experience gained with Yutu-2 improves the understanding of the farside of the Moon, which, in return, may lead to locomotion with improved efficiency and larger range. Description Yutu-2 conducted in situ exploration on the lunar farside, yielding insights into lunar regolith, craters, and rocks.

A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover

[Identification of volatile compounds of hawthorn by gas chromatography/mass spectrometry (GC/MS)].

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A 2-year locomotive exploration and scientific investigation of the lunar farside by the Yutu-2 rover

[Identification of volatile compounds of hawthorn by gas chromatography/mass spectrometry (GC/MS)].