First results from the ROBEX Demonstration Mission on Mt. Etna: Robotic deployment of seismic networks for future lunar missions

Wedler, Armin

doi:10.26226/morressier.59c106e8d462b80292389b74

Cited by 20 publications

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“…The two main missions were to install a seismic network and to perform a seismic profile measurement. A number of other experiments aimed to demonstrate the capabilities in autonomous navigation, multirobot mapping, crater exploration, geological sample investigation, and probe placement (Wedler et al, 2017, 2018). Its natural seismic activity and volcanic origin, as well as its representativeness of lunar surfaces with respect to topography and morphology fulfilled the criteria of Moon‐analogousness.…”

Section: Field Experimentsmentioning

confidence: 99%

ARDEA—An MAV with skills for future planetary missions

Lutz

Müller

Maier

et al. 2020

Journal of Field Robotics

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We introduce a prototype flying platform for planetary exploration: autonomous robot design for extraterrestrial applications (ARDEA). Communication with unmanned missions beyond Earth orbit suffers from time delay, thus a key criterion for robotic exploration is a robot's ability to perform tasks without human intervention. For autonomous operation, all computations should be done on-board and GlobalNavigation Satellite System (GNSS) should not be relied on for navigation purposes.Given these objectives ARDEA is equipped with two pairs of wide-angle stereo cameras and an inertial measurement unit (IMU) for robust visual-inertial navigation and time-efficient, omni-directional 3D mapping. The four cameras cover a 240 ∘ vertical field of view, enabling the system to operate in confined environments such as caves formed by lava tubes. The captured images are split into several pinhole cameras, which are used for simultaneously running visual odometries. The stereo output is used for simultaneous localization and mapping, 3D map generation and collision-free motion planning. To operate the vehicle efficiently for a variety of missions, ARDEA's capabilities have been modularized into skills which can be assembled to fulfill a mission's objectives. These skills are defined generically so that they are independent of the robot configuration, making the approach suitable for different heterogeneous robotic teams. The diverse skill set also makes the micro aerial vehicle (MAV) useful for any task where autonomous exploration is needed.For example terrestrial search and rescue missions where visual navigation in GNSSdenied indoor environments is crucial, such as partially collapsed man-made structures like buildings or tunnels. We have demonstrated the robustness of our system in indoor and outdoor field tests. K E Y W O R D S aerial robotics, computer vision, exploration, GPS-denied operation, planetary robotics ---This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Field Experimentsmentioning

confidence: 99%

ARDEA—An MAV with skills for future planetary missions

Lutz

Müller

Maier

et al. 2020

Journal of Field Robotics

Self Cite

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“…As real orbit-to-ground experiments are rarely available, researchers evaluate approaches for robotic teleoperation in analogue experiments. These experiments often aim to mimic the extraterrestrial environment of the robot (e. g. [9], [10]) or challenging communication links (e. g. [11], [12]). The remainder of this section gives an overview of past orbit-toground teleoperation experiments.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Driven Orbit-to-Ground Teleoperation of a Robot Coworker

Schmaus

Leidner

Krüger

et al. 2020

IEEE Robot. Autom. Lett.

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The crewed exploration of Moon and Mars requires the construction and maintenance of infrastructure on the alien surfaces before a crew arrives. Robotic coworkers are envisioned to take over the physical labor required to set-up crew habitats, energy supplies, and return vehicles in the hazardous environment. Deploying these robots in such a remote location poses a challenge that requires autonomous robot capabilities in combination with effective Human Robot Interfaces (HRIs), which comply with the harsh conditions of deep space operations. An astronaut-robot teleoperation concept targeting these topics has been evaluated in DLR and ESA's METERON SUPVIS Justin experiment where astronauts on-board the International Space Station (ISS) commanded DLR's humanoid robot Rollin' Justin in a simulated Martian environment on Earth. This work extends on our previously presented approach to supervised autonomy. It examines the results of the two follow-up experiment sessions which investigated maintenance and assembly tasks in real-world scenarios. We discuss the use of our system in real space-toground deployment and analyze key performance metrics of the HRI and the feedback given by the astronauts.

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“…We thus did not utilize any static landmarks in the experiments presented here. However, for planetary exploration, it is reasonable to use static structures, such as a stationary lander (Wedler et al, ), as landmarks to improve localization and provide transformations between multiple robots. Submap matches

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represents the transformation between the origins of two submaps, which is the result of our submap matching process, described in detail in Section . This can lead to intra‐ as well as inter‐robot loop closures. In addition, we integrate the estimates of our local reference filters, thereby connecting robot and submap poses.…”

Section: Incremental Multi‐robot Graph Slammentioning

confidence: 99%

Section: Incremental Multi‐robot Graph Slammentioning

confidence: 99%

“…As an important aspect with regard to robot navigation, the volcanic rough‐terrain environment is also visually similar to the surface of the Moon. In the ROBEX main experiment, one of our LRU roves autonomously picked up seismic measurement instruments from a lander mockup and deployed them at predefined target locations, see Wedler et al () for details. We successfully used our localization and mapping framework for single‐robot local and global localization and mapping during this mission and used the test site for an additional multi‐robot exploration experiment.…”

Section: Demonstration In Moon‐analogue Environmentmentioning

confidence: 99%

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Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Schuster

Schmid²,

Brand

et al. 2018

Journal of Field Robotics

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Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, longterm stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra-as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five realworld multi-robot experiments in areas of up 3,000 m 2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano. K E Y W O R D S graph SLAM, map matching, mobile robots, multi-robot, navigation filter 1 | INTRODUCTION The exploration of moons and foreign planets is an important current and future application for mobile robots as their surfaces are difficult to reach and hard to access for humans. The application of huge and complex robot systems such as Curiosity, landed on Mars in 2012, creates many single points of failure for a mission. As a consequence, these rovers have to move very slowly and carefully to avoid getting stuck, as the Mars rover Spirit did in 2009 (Wolchover, 2011). The future deployment of teams of multiple robots can avoid these single points of failure by gaining robustness through redundancy and, in addition, can improve efficiency through parallelization. The robots have to travel through previously unknown unstructured rough terrain, operating in areas where external methods for localization like global navigation satellite systems (GNSS) are not available or expensive to set up. Communication links to the robots are limited and heavily delayed, featuring for example 8-40 min round trip time between Earth andMars. Furthermore, communication between the robots cannot be guaranteed at all times, in particular at scientifically interesting places such as craters, canyons, or caves. As teleoperation therefore becomes inefficient or infeasible, robot autonomy is a key aspect for future planetary exploration missions. Any coordinated (semi-)autonomous operation in such challenging environments requires up-to-date localization estimates for all robots in a team as well as a joint map to operate on.

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First results from the ROBEX Demonstration Mission on Mt. Etna: Robotic deployment of seismic networks for future lunar missions

Cited by 20 publications

References 0 publications

ARDEA—An MAV with skills for future planetary missions

ARDEA—An MAV with skills for future planetary missions

Knowledge Driven Orbit-to-Ground Teleoperation of a Robot Coworker

Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

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