Soft Gripping System for Space Exploration Legged Robots

Although wheeled robots have been predominant for planetary exploration, their geometry limits their capabilities when traveling over steep slopes, through rocky terrains, and in microgravity. Legged robots equipped with grippers are a viable alternative to overcome these obstacles. This paper proposes a gripping system that can provide legged space-explorer robots a reliable anchor on uneven rocky terrain. This gripper provides the benefits of soft gripping technology by using segmented tendon-driven fingers to adapt to the target shape, and creates a strong adhesion to rocky surfaces with the help of microspines. The gripping performances are showcased, and multiple experiments demonstrate the impact of the pulling angle, target shape, spine configuration, and actuation power on the performances. The results show that the proposed gripper can be a suitable solution for advanced space exploration, including climbing, lunar caves, or exploration of the surface of asteroids.

Modeling of Terrain Deformation by a Grouser Wheel for Lunar Rover Simulation

Simulation of vehicle motion in planetary environments is challenging. This is due to the modeling of complex terrain, optical conditions, and terrain-aware vehicle dynamics. One of the critical issues of typical simulators is that they assume terrain is a rigid body, which limits their ability to render wheel traces and compute the wheel-terrain interactions. This prevents, for example, the use of wheel traces as landmarks for localization, as well as the accurate simulation of motion. In the context of lunar regolith, the surface is not rigid but granular. As such, there are differences in the rover's motion, such as sinkage and slippage, and a clear wheel trace left behind the rover, compared to that on a rigid terrain. This study presents a novel approach to integrating a terramechanics-aware terrain deformation engine to simulate a realistic wheel trace in a digital lunar environment. By leveraging Discrete Element Method simulation results alongside experimental single-wheel test data, we construct a regression model to derive deformation height as a function of contact normal force. The region of interest in a height map is retrieved from the wheel poses. The elevation values of corresponding pixels are subsequently modified using contact normal forces and the regression model. Finally, we apply the determined elevation change to each mesh vertex to render wheel traces during runtime. The deformation engine is integrated into our ongoing development of a lunar simulator based on NVIDIA's Omniverse IsaacSim. We hypothesize that our work will be crucial to testing perception and downstream navigation systems under conditions similar to outdoor or terrestrial fields. A demonstration video is available here: https://www.youtube.com/watch?v=TpzD0h-5hv4

Structure from Motion-based Motion Estimation and 3D Reconstruction of Unknown Shaped Space Debris

With the boost in the number of spacecraft launches in the current decades, the space debris problem is daily becoming significantly crucial. For sustainable space utilization, the continuous removal of space debris is the most severe problem for humanity. To maximize the reliability of the debris capture mission in orbit, accurate motion estimation of the target is essential. Space debris has lost its attitude and orbit control capabilities, and its shape is unknown due to the break. This paper proposes the Structure from Motion-based algorithm to perform unknown shaped space debris motion estimation with limited resources, where only 2D images are required as input. The method then outputs the reconstructed shape of the unknown object and the relative pose trajectory between the target and the camera simultaneously, which are exploited to estimate the target's motion. The method is quantitatively validated with the realistic image dataset generated by the microgravity experiment in a 2D air-floating testbed and 3D kinematic simulation.

Performance Comparison of ROS2 Middlewares for Multi-robot Mesh Networks in Planetary Exploration

Recent advancements in Multi-Robot Systems (MRS) and mesh network technologies pave the way for innovative approaches to explore extreme environments. The Artemis Accords, a series of international agreements, have further catalyzed this progress by fostering cooperation in space exploration, emphasizing the use of cutting-edge technologies. In parallel, the widespread adoption of the Robot Operating System 2 (ROS 2) by companies across various sectors underscores its robustness and versatility. This paper evaluates the performances of available ROS 2 MiddleWare (RMW), such as FastRTPS, CycloneDDS and Zenoh, over a mesh network with a dynamic topology. The final choice of RMW is determined by the one that would fit the most the scenario: an exploration of the extreme extra-terrestrial environment using a MRS. The conducted study in a real environment highlights Zenoh as a potential solution for future applications, showing a reduced delay, reachability, and CPU usage while being competitive on data overhead and RAM usage over a dynamic mesh topology

Space Debris Reliable Capturing by a Dual-Arm Orbital Robot: Detumbling and Caging

A chaser satellite equipped with robotic arms can capture space debris and manipulate it for use in more advanced missions such as refueling and deorbiting. To facilitate capturing, a caging-based strategy has been proposed to simplify the control system. Caging involves geometrically constraining the motion of the target debris, and is achieved via position control. However, if the target is spinning at a high speed, direct caging may result in unsuccessful constraints or hardware destruction; therefore, the target should be de-tumbled before capture. To address this problem, this study proposes a repeated contact-based method that uses impedance control to mitigate the momentum of the target. In this study, we analyzed the proposed detumbling technique from the perspective of impedance parameters. We investigated their effects through a parametric analysis and demonstrated the successful detumbling and caging sequence of a microsatellite as representative of space debris. The contact forces decreased during the detumbling sequence compared with direct caging. Further, the proposed detumbling and caging sequence was validated through simulations and experiments using a dual-arm air-floating robot in two-dimensional microgravity emulating testbed.

Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features into motion cost calculations. The algorithm also integrates localization and mapping to update the rover's pose and map the environment. The resulting environment map's accuracy is evaluated and tested in a 3D simulator. Outdoor field tests were conducted to validate the algorithm's efficacy in sim-to-real scenarios. The results showed that the algorithm could achieve high coverage with low energy consumption and computational cost, while incrementally exploring the terrain and avoiding obstacles. This study contributes to the advancement of path planning methodologies for space exploration, paving the way for efficient, scalable and autonomous exploration of lunar environments by small rovers.

Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots

Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, this study presents a tightly-coupled LiDAR-IMU-wheel odometry algorithm with an online calibration for skid-steering robots. We propose a full linear wheel odometry factor, which not only serves as a motion constraint but also performs the online calibration of kinematic models for skid-steering robots. Despite the dynamically changing kinematic model (e.g., wheel radii changes caused by tire pressures) and terrain conditions, our method can address the model error via online calibration. Moreover, our method enables an accurate localization in cases of degenerated environments, such as long and straight corridors, by calibration while the LiDAR-IMU fusion sufficiently operates. Furthermore, we estimate the uncertainty (i.e., covariance matrix) of the wheel odometry online for creating a reasonable constraint. The proposed method is validated through three experiments. The first indoor experiment shows that the proposed method is robust in severe degeneracy cases (long corridors) and changes in the wheel radii. The second outdoor experiment demonstrates that our method accurately estimates the sensor trajectory despite being in rough outdoor terrain owing to online uncertainty estimation of wheel odometry. The third experiment shows the proposed online calibration enables robust odometry estimation in changing terrains.

Lower Gravity Demonstratable Testbed for Space Robot Experiments

In developing mobile robots for exploration on the planetary surface, it is crucial to evaluate the robot's performance, demonstrating the harsh environment in which the robot will actually be deployed. Repeatable experiments in a controlled testing environment that can reproduce various terrain and gravitational conditions are essential. This paper presents the development of a minimal and space-saving indoor testbed, which can simulate steep slopes, uneven terrain, and lower gravity, employing a three-dimensional target tracking mechanism (active xy and passive z) with a counterweight.

OmniLRS: A Photorealistic Simulator for Lunar Robotics

Developing algorithms for extra-terrestrial robotic exploration has always been challenging. Along with the complexity associated with these environments, one of the main issues remains the evaluation of said algorithms. With the regained interest in lunar exploration, there is also a demand for quality simulators that will enable the development of lunar robots. % In this paper, we explain how we built a Lunar simulator based on Isaac Sim, Nvidia's robotic simulator. In this paper, we propose Omniverse Lunar Robotic-Sim (OmniLRS) that is a photorealistic Lunar simulator based on Nvidia's robotic simulator. This simulation provides fast procedural environment generation, multi-robot capabilities, along with synthetic data pipeline for machine-learning applications. It comes with ROS1 and ROS2 bindings to control not only the robots, but also the environments. This work also performs sim-to-real rock instance segmentation to show the effectiveness of our simulator for image-based perception. Trained on our synthetic data, a yolov8 model achieves performance close to a model trained on real-world data, with 5% performance gap. When finetuned with real data, the model achieves 14% higher average precision than the model trained on real-world data, demonstrating our simulator's photorealism.% to realize sim-to-real. The code is fully open-source, accessible here: https://github.com/AntoineRichard/LunarSim, and comes with demonstrations.

Integration of Vision-based Object Detection and Grasping for Articulated Manipulator in Lunar Conditions

The integration of vision-based frameworks to achieve lunar robot applications faces numerous challenges such as terrain configuration or extreme lighting conditions. This paper presents a generic task pipeline using object detection, instance segmentation and grasp detection, that can be used for various applications by using the results of these vision-based systems in a different way. We achieve a rock stacking task on a non-flat surface in difficult lighting conditions with a very good success rate of 92%. Eventually, we present an experiment to assemble 3D printed robot components to initiate more complex tasks in the future.