Rover Path Planning Research Articles

Enhanced Interactive Rendering for Rovers of Lunar Polar Region and Martian Surface

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover’s interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover’s traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover’s path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps.

JPL/Caltech ArtCenter

Abstract Data visualization frequently provides audiences with novel semantic and computational presentations. How does a multifaceted team expand this scope by harnessing the power of visualization as a tool to think with? The NASA JPL/Caltech/ ArtCenter data visualization program demonstrates how scientific knowledge, shaped from data and theory, is equally co-constructed from diverse human perspectives. We will share case studies from Mars Rover Path planning and PIXLISE, a visual reasoning tool for understanding planetary geology. Working from source data through mixed media artifacts, these projects explore co-design methods for complex scientific domains with real-world applications. Our methodology emphasizes that all participants in the co-design process are both learners and experts. In this dynamic, the design and coding process are unique modes of critical discovery.

APPLICATIONS OF PHOTOGRAMMETRY AND REMOTE SENSING TECHNOLOGIES IN CHANG’E-4 MISSION

Abstract. This paper presents a brief overview of photogrammetry and remote sensing technologies and applications in the Chang’e-4 mission. The developed technologies, such as image matching, block adjustment, 3D mapping, spectral analysis and mineral retrieval, have been applied in topographic mapping, rover localization, and rover path planning, and directly supported daily surface operations of the Yutu-2 rover. The technologies and products have also been supportive in various scientific investigations, such as topographic evolution of Von Kármán crater, multi-scale crater morphologies, mineral composition of lunar deep materials, regolith maturity and space weathering effects, etc.

Planetary Rover Path Planning Method with Robustness against Digital Elevation Model's Uncertainty

Planetary rovers need to plan a safe route to ensure the success of their mission. Many missions to the Moon and Mars use existing remote sensing data to plan their exploration routes before launch under the assumption that the data is correct. For example, digital elevation models (DEMs) are biased in their accuracy depending on the density of their source data. Most studies of advance path planning using DEMs do not account for that uncertainty. Therefore, the path planned in advance is not necessarily safe for the actual rover. In this paper, we propose a path planning method that is robust against the uncertainty of terrain data generated from remote sensing data. Simulations using real data confirmed that the route planned by this method is safer than these two conventional methods: 1) route planning that assumes the topographic data is correct, and 2) route planning that adds Gaussian noise to the DEM to generate multiple DEMs and treats their variance as uncertainty.

Value Iteration Networks with Double Estimator for Planetary Rover Path Planning.

Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN suffers from several limitations, including sensitivity to initialization and poor performance in large-scale domains. We introduce the double value iteration network (dVIN), which decouples action selection and value estimation in the VI module, using the weighted double estimator method to approximate the maximum expected value, instead of maximizing over the estimated action value. We have devised a simple, yet effective, two-stage training strategy for VI-based models to address the problem of high computational cost and poor performance in large-size domains. We evaluate the dVIN on planning problems in grid-world domains and realistic datasets, generated from terrain images of a moon landscape. We show that our dVIN empirically outperforms the baseline methods and generalize better to large-scale environments.

Terrain classification-based rover traverse planner with kinematic constraints for Mars exploration

In Mars exploration mission, the rover path planning is essential to ensure the safety and efficiency of the rover traverse. At present, traverse planning deployed Mars rovers requires experienced geologists and planetary scientists to spend a lot of time on surface-topography analysis. To a large extent, this is a costly and time-consuming manual process. Meanwhile, traditional optimal search algorithms (A∗ and Dijkstra) can search for a globally optimal path, but the path found does not satisfy the nonholonomic constraint of the vehicle, thus not directly drivable. The Terrain Classification-based Rover Traverse Planner (TCRTP), introduced in this article, is an automated algorithm that uses deep learning-based terrain classification as the guide to generate the optimal traverses with kinematic constraints. TCRTP combines the terrain classification with nonholonomic constraints, and it can quickly and accurately obtain the global optimal solution under the heuristic function. We also propose modifications of the cost function for path optimization to improve the smoothness of the solution. This algorithm plans forward and reverse movement, and penalizes reverse driving and changing the direction of movement. The TCRTP algorithm is easy to be extended, and the optimization traversal under multiple missions can be realized by adding traffic constraints, such as terrain roughness, elevation changes and so on. The TCRTP-based rover allows for real-time and frequent planning, and the addition of terrain constraints ensures the safety of the planned route. Our planner, combined with computer vision, shows the potential to reduce operator workloads and explore future planetary space.

COMPUTER VISION IN THE TELEOPERATION OF THE YUTU-2 ROVER

Abstract. On January 3, 2019, the Chang'e-4 (CE-4) probe successfully landed in the Von Kármán crater inside the South Pole-Aitken (SPA) basin. With the support of a relay communication satellite "Queqiao" launched in 2018 and located at the Earth-Moon L2 liberation point, the lander and the Yutu-2 rover carried out in-situ exploration and patrol surveys, respectively, and were able to make a series of important scientific discoveries. Owing to the complexity and unpredictability of the lunar surface, teleoperation has become the most important control method for the operation of the rover. Computer vision is an important technology to support the teleoperation of the rover. During the powered descent stage and lunar surface exploration, teleoperation based on computer vision can effectively overcome many technical challenges, such as fast positioning of the landing point, high-resolution seamless mapping of the landing site, localization of the rover in the complex environment on the lunar surface, terrain reconstruction, and path planning. All these processes helped achieve the first soft landing, roving, and in-situ exploration on the lunar farside. This paper presents a high-precision positioning technology and positioning results of the landing point based on multi-source data, including orbital images and CE-4 descent images. The method and its results have been successfully applied in an actual engineering mission for the first time in China, providing important support for the topographical analysis of the landing site and mission planning for subsequent teleoperations. After landing, a 0.03 m resolution DOM was generated using the descent images and was used as one of the base maps for the overall rover path planning. Before each movement, the Yutu-2 rover controlled its hazard avoidance cameras (Hazcam), navigation cameras (Navcam), and panoramic cameras (Pancam) to capture stereo images of the lunar surface at different angles. Local digital elevation models (DEMs) with a 0.02 m resolution were routinely produced at each waypoint using the Navcam and Hazcam images. These DEMs were then used to design an obstacle recognition method and establish a model for calculating the slope, aspect, roughness, and visibility. Finally, in combination with the Yutu-2 rover mobility characteristics, a comprehensive cost map for path search was generated.By the end of the first 12 lunar days, the Yutu-2 rover has been working on the lunar farside for more than 300 days, greatly exceeding the projected service life. The rover was able to overcome the complex terrain on the lunar farside, and travelled a total distance of more than 300 m, achieving the "double three hundred" breakthrough. In future manned lunar landing and exploration of Mars by China, computer vision will play an integral role to support science target selection and scientific investigations, and will become an extremely important core technology for various engineering tasks.

Vision-Based Decision Support for Rover Path Planning in the Chang’e-4 Mission

In planetary rover missions, rover path planning is critical to ensure the safety and efficiency of the rover traverse and in situ explorations. In the Chang’e-4 (CE-4) mission, we have proposed and developed vision-based decision support methods comprising obstacle map generation and path evaluation for the rover. At each waypoint along the rover traverse, digital elevation model (DEM) is automatically generated, which is then used for obstacle map generation and path searching. For path evaluation, the searched path and the predicted wheel trajectories are projected onto the original images captured by different cameras to coincide with the real observation scenario. The proposed methods have been applied in the CE-4 mission to support teleoperation of the rover, and examples of multiple applications used in the mission are presented in this paper. Under the support of the proposed techniques, by the end of the 14th lunar day (3 February 2020), the rover has already travelled 367.25 meters on the far side of the Moon.

Rover-IRL: Inverse Reinforcement Learning With Soft Value Iteration Networks for Planetary Rover Path Planning

Planetary rovers, such as those currently on Mars, face difficult path planning problems, both before landing during the mission planning stages as well as once on the ground. In this work, we present a new approach to these planning problems based on inverse reinforcement learning using deep convolutional networks and value iteration networks (VIN) as important internal structures. VIN are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. We propose a modification to the value iteration recurrence, referred to as the soft value iteration network (SVIN). SVIN is designed to produce more effective training gradients through the VIN. It relies on an internal soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action. We demonstrate the effectiveness of our proposed architecture in both a grid world dataset as well as a highly realistic synthetic dataset generated from currently deployed rover mission planning tools and real Mars imagery.

Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT*algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT*. This process improves conventional RRT*in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

High precision landing site mapping and rover localization for Chang’e-3 mission

This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission. High-precision topographic products of the landing site with extremely high resolutions (up to 0.05 m) were generated from descent images and registered to CE-2 DOM. Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse. The lander location was determined to be (19.51256°W, 44.11884°N, −2615.451 m) using a method of DOM matching. In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning, cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints; the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization. The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable. During CE-3 mission operations, landing site mapping and rover localization products including DEMs and DOMs, traverse maps, vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.