Robotic Deployment of Extraterrestrial Seismic Networks

Manual installation of seismic networks in extraterrestrial environments is risky, expensive and error-prone. A more reliable alternative is the automated deposition with a light-weight robot manipulator. However, inserting a spiked sensor into soil is a challenging task for a robot since the soil parameters are variable and difficult to estimate. Therefore, we investigate an approach to accurate insertion and positioning of geophones using a Cartesian impedance controller with a feed-forward force term. The feed-forward force component of the controller is either estimated using the Fundamental Earth-Moving Equation, the Discrete Element Method or empirically. For the first time, both the geological aspects of the problem as well as the aspects of robotic control are considered. Based on this consideration, the control approach is enhanced by predicting the resistance force of the soil. Experiments with the humanoid robot Rollin’ Justin inserting a geophone into three different soil samples validate the proposed method

Slip Modeling and Estimation for a Planetary Exploration Rover: Experimental Results from Mt. Etna

For wheeled mobile systems, the wheel odometry is an important source of information about the current motion of the vehicle. It is used e.g. in the context of pose estimation and self-localization of planetary rovers, which is a crucial part of the success of planetary exploration missions. Depending on the wheel-soil interaction properties, wheel odometry measurements are subject to inherent errors such as wheel slippage. In this paper, a parameter-based approach for whole-body slip modeling and calibration is applied to a four-wheeled lightweight rover system. Details on the method for slip parameter calibration as well as the system-specific implementation are given. Experimental results from a test campaign on Mt. Etna are presented, showing significant improvements of the resulting wheel odometry measurements. The results are validated during a long range drive of approx. 900 m and discussed w. r. t. the advantages but also limitations of the method within a space exploration scenario

Datasets and Benchmarking of a path planning pipeline for planetary rovers

We present datasets of 2.5D elevation maps of planetary environment that were collected on Mt. Etna during the space-analogous ARCHES mission [1]. In addition to the raw elevation maps, we provide cost maps that encode the traversibility of the terrain. We demonstrate how these cost maps are used during our development of mapping and planning algorithms for ground based robots in the context of the planetary rover navigation. More specifically, we use the benchmarking pipeline to evaluate the parameters and choice of methods that are used for the 2.5D cost map generation, which in turn affects the path planning behavior. Finally, we showcase how the provided maps can be supplied as a test environment in Bench-MR, which is a framework for benchmarking of motion planning algorithms for wheeled robots

Terrain-Aware Communication Coverage Prediction for Cooperative Networked Robots in Unstructured Environments

Networked robots will play an important role in lunar exploration. Communication is key to enable cooperation among robots for information sharing, and to remotely control robots with lower degree of autonomy from a lander or habitat. Operators and scientists must be able to make sound decisions on communication availability before or during sending robots to regions of interest for exploration. In this work we have a closer look at the communication coverage prediction for lunar exploration. We present an interdisciplinary and modular framework, which exploits terrain information to predict the data rate for exploring robots. Additionally, we create intuitively usable coverage maps for operators and scientists, and show how connectivity can be improved in unstructured environments by using a relay rover. This paper provides an overview of this framework, details on individual framework components, and simulation results for two exemplary exploration scenarios

Terrain-Aware Communication Coverage Prediction for Cooperative Networked Robots in Unstructured Environments

Networked robots will play an important role in lunar exploration. Communication is key to enable cooperation among robots for information sharing, and to remotely control robots with lower degree of autonomy from a lander or habitat. Operators and scientists must be able to make sound decisions on communication availability before or during sending robots to regions of interest for exploration. In this work we have a closer look at the communication coverage prediction for lunar exploration. We present an interdisciplinary and modular framework, which exploits terrain information to predict the data rate for exploring robots. Additionally, we create intuitively usable coverage maps for operators and scientists, and show how connectivity can be improved in unstructured environments by using a relay rover. This paper provides an overview of this framework, details on individual framework components, and simulation results for two exemplary exploration scenarios

Modular Mechatronics Infrastructure for Robotic Planetary Exploration Assets in a Field Operation Scenario

In 2021 the Modular Mechatronics Infrastructure (MMI) was introduced as a solution to reduce weight, costs, and development time in robotic lanetary missions. With standardized interfaces and multi-functional elements, this modular approach is planned to be used more often in sustainable exploration activities on the Moon and Mars. The German multi-robot research project “Autonomous Robotic Networks to Help Modern Societies (ARCHES)” has explored this concept with the use of various collaborative robotic assets which have their capabilities extended by the MMI. Different scientific payloads, engineering infrastructure modules, and specific purpose tools can be integrated to and manipulated by a robotic arm and a standardized electromechanical docking-interface. Throughout the MMI’s design and implementation phase the performed preliminary tests confirmed that the different systems of the robotic cooperative team such as the Docking Interface System (DIS), the Power Management System (PMS), and the Data Communication System (DCS) functioned successfully. During the summer of 2022 a Demonstration Mission on Mount Etna (Sicily, Italy) was carried out as part of the ARCHES Project. This field scenario allowed the validation of the robotics systems in an analogue harsh environment and the confirmation of enhanced operations with the application of this modular method. Among the numerous activities performed in this volcanic terrain there are the efficient assembling of the Low Frequency Array (LOFAR) network, the energy-saving and reduced complexity of a detached Laser Induced Breakdown Spectroscopy (LIBS) module, and the uninterrupted powered operation between modules when switching between different power sources. The field data collected during this analogue campaign provided important outcomes for the modular robotics application. Modular and autonomous robots certainly benefit from their versatility, reusability, less complex systems, reduced requirements for space qualification, and lower risks for the mission. These characteristics will ensure that long duration and complex robotic planetary endeavours are not as challenging as they used to be in the past

Autonomous Rock Instance Segmentation for Extra-Terrestrial Robotic Missions

The collection and analysis of extra-terrestrial matter are two of the main motivations for space exploration missions. Due to the inherent risks for participating astronauts during space missions, autonomous robotic systems are often consid- ered as a promising alternative. In recent years, many (in- ter)national space missions containing rovers to explore celestial bodies have been launched. Hereby, the communication delay as well as limited bandwidth creates a need for highly self-governed agents that require only infrequent interaction with scientists at a ground station. Such a setting is explored in the ARCHES mis- sion, which seeks to investigate different means of collaboration between scientists and autonomous robots in extra-terrestrial environments. The analog mission focuses a team of hetero- geneous agents (two Lightweight Rover Units and ARDEA, a drone), which together perform various complex tasks under strict communication constraints. In this paper, we highlight three of these tasks that were successfully demonstrated during a one-month test mission on Mt. Etna in Sicily, Italy, which was chosen due to its similarity to the Moon in terms of geological structure. All three tasks have in common, that they leverage an instance segmentation approach deployed on the rovers to detect rocks within camera imagery. The first application is a map- ping scheme that incorporates semantically detected rocks into its environment model to safely navigate to points of interest. Secondly, we present a method for the collection and extraction of in-situ samples with a rover, which uses rock detection to localize relevant candidates to grasp. For the third task, we show the usefulness of stone segmentation to autonomously conduct a spectrometer measurement experiment. We perform a throughout analysis of the presented methods and evaluate our experimental results. The demonstrations on Mt. Etna show that our approaches are well suited for navigation, geological analysis, and sample extraction tasks within autonomous robotic extra-terrestrial missions

Enabling Distributed Low Radio Frequency Arrays - Results of an Analog Campaign on Mt. Etna

Measurement of the red-shifted 21-cm signal of neutral hydrogen, and thus observing The Dark Ages is expected to be the holy grail of 21-cm Cosmology. A Radio-telescope to observe low radio frequency signals is needed, but radio interference on Earth and Earth's ionosphere blocking these signals are limiting science investigations in this field. Hence, such a radio-telescope composed of dozens to hundreds of antennas shall be deployed on the lunar far side. Such arrays are shielded from interference from Earth and Earth's ionosphere blocking very low radio frequencies is not present. Within the Helmholtz Future Topic Project Autonomous Robotic Networks to Help Modern Societies (ARCHES) we developed necessary technologies for autonomous robotic deployment of antenna elements, modular payload box design, and robust radio-localization to enable such distributed low-frequency arrays. In particular the antennas’ positions must be determined accurately, such that the array can be operated as phased array. Our developments lead to the execution of an analog-demonstration on the volcano Mt. Etna, Sicily, Italy, in June and July 2022 over the course of four weeks. We successfully demonstrated the autonomous robotic deployment of antenna elements and our decentralized real-time radio-localization system to obtain the antenna element positions. Additionally, we showed a proof-of-concept operation of the phased array comprising four antenna elements: estimating the signal direction of arrival of a radio-beacon with unknown position, and the beamforming capabilities itself, for a carrier frequency of 20 MHz. In this paper, we give insights into our developed technologies and the analog-demonstration on the volcano Mt. Etna, Sicily, Italy. We show results of the successfully executed mission and give an outlook how our developed technologies can be further used for lunar exploration

Robust place recognition with Gaussian Process Gradient Maps for teams of robotic explorers in challenging lunar environments

Teams of mobile robots will play a key role towards future planetary exploration missions. In fact, plans for upcoming lunar exploration, and other extraterrestrial bodies, foresee an extensive usage of robots for the purposes of in-situ analysis, building infrastructure and realizing maps of the environment for its exploitation. To enable prolonged robotic autonomy, however, it is critical for the robotic agents to be able to robustly localize themselves during their motion and, concurrently, to produce maps of the environment. To this end, visual SLAM (Simultaneous Localization and Mapping) techniques have been developed during the years and found successful application in several terrestrial fields, such as autonomous driving, automated construction and agricultural robotics. To this day, autonomous navigation has been demonstrated in various robotic missions to Mars, e.g., from NASA's Mars Exploration Rover (MER) Missions, to NASA's Mars Science Laboratory (Curiosity) and the current Mars2020 Perseverance, thanks to the implementation of Visual Odometry, using cameras to robustly estimate the rover's ego-motion. While VO techniques enable the traversal of large distances from one scientific target to the other, future operations, e.g., for building or maintenance of infrastructure, will require robotic agents to repeatedly visit the same environment. In this case, the ability to re-localize themselves with respect to previously visited places, and therefore the ability to create consistent maps of the environment, is paramount to achieve localization accuracies, that are far above what is achievable from global localization approaches. The planetary environment, however, poses significant challenges to this goal, due to extreme lighting conditions, severe visual aliasing and a lack of uniquely identifiable natural "features". For this reason, we developed an approach for re-localization and place recognition, that relies on Gaussian Processes, to efficiently represent portions of the local terrain elevation, named "GPGMaps" (Gaussian Process Gradient Maps), and to use its gradient in conjunction with traditional visual matching techniques. In this paper, we demonstrate, analyze and report the performances of our SLAM approach, based on GPGMaps, during the 2022 ARCHES (Autonomous Robotic Networks to Help Modern Societies) mission, that took place on the volcanic ash slopes of Mt. Etna, Sicily, a designated planetary analogous environment. The proposed SLAM system has been deployed for real-time usage on a robotic team that includes the LRU (Lightweight Rover Unit), a planetary-like rover with high autonomy, perceptual and locomotion capabilities, to demonstrate enabling technologies for future lunar applications