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Exploring Event Camera-Based odometry for Planetary Robots

IEEE robotics AND automation LETTERS 2022年第4期7卷 8651-8658页

作者： Mahlknecht, Florian Gehrig, Daniel Nash, Jeremy Rockenbauer, Friedrich M. Morrell, Benjamin Delaune, Jeff Scaramuzza, Davide CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA Univ Zurich Robot & Percept Grp CH-8050 Zurich Switzerland

Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle, since they cannot cope with significant depth uncertainties caused by an unforeseen loss of tracking or other effects. In this work, we introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based frontend with a filter-based backend. This makes it both accurate and robust to uncertainties, outperforming eventand frame-based VIO algorithms on challenging benchmarks by 32%. In addition, we demonstrate accurate performance in hoverlike conditions (outperforming existing event-based methods) as well as high robustness in newly collected Mars-like and high-dynamic-range sequences, where existing frame-based methods fail. In doing so, we show that event-based VIO is the way forward for vision-based exploration on Mars.

关键词： Vision-based navigation space robotics and automation visual-inertial SLAM

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An Approach to Science and Risk-Aware Planetary Rover Exploration

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IEEE robotics AND automation LETTERS 2022年第4期7卷 9691-9698页

作者： Candela, Alberto Wettergreen, David Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

This work grapples with the challenge of directing autonomous decision making by planetary rovers conducting science investigations. Most of the related work addresses obstacle avoidance and traversabilty, while less work seeks to directly improve science yield. This research develops a comprehensive approach for planetary rovers that accounts for both science investigation and mobility risk. We present a probabilistic framework that quantifies these two attributes of rover exploration and generates paths that constrain risk while increasing science return. Specifically, science productivity is measured and improved using formal principles from information theory and statistical learning for decision making. Risk is estimated using a probabilistic model that predicts rover wheel slippage based on geometric and semantic information. Our method is evaluated in a simulation study using real Mars surface data that is relevant for both science and terrain investigations. Experimental analysis verifies the effectiveness of our approach.

关键词： Motion and path planning probability and statistical methods space robotics and automation

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The RATTLE Motion Planning Algorithm for Robust Online Parametric Model Improvement With On-Orbit Validation

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IEEE robotics AND automation LETTERS 2022年第4期7卷 10946-10953页

作者： Albee, Keenan Ekal, Monica Coltin, Brian Ventura, Rodrigo Linares, Richard Miller, David W. MIT Dept Aeronaut & Astronaut Cambridge MA 02142 USA Inst Super Tecn Inst Syst & Robot P-1049001 Lisbon Portugal NASA SGT Inc Ames Res Ctr Moffett Field CA 94035 USA

Certain forms of uncertainty that robotic systems encounter can be explicitly learned within the context of a known model, like parametric model uncertainties such as mass and moments of inertia. Quantifying such parametric uncertainty is important for more accurate prediction of the system behavior, leading to safe and precise task execution. In tandem, providing a form of robustness guarantee against prevailing uncertainty levels like environmental disturbances and current model knowledge is also desirable. To that end, the authors' previously proposed RATTLE algorithm, a framework for online information-aware motion planning, is outlined and extended to enhance its applicability to real robotic systems. RATTLE provides a clear tradeoff between information-seeking motion and traditional goal-achieving motion and features online-adjustable models. Additionally, online-adjustable low level control robustness guarantees and a new method for automatic adjustment of information content down to a specified estimation precision is proposed. Results of extensive experimentation in microgravity using the Astrobee robots aboard the International space Station and practical implementation details are presented, demonstrating RATTLE's capabilities for real-time, robust, online-adjustable, and model information-seeking motion planning capabilities under parametric uncertainty.

关键词： Constrained motion planning information-aware planning model learning for control model predictive control space robotics and automation

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Active Exploitation of Redundancies in Reconfigurable Multirobot Systems

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IEEE TRANSACTIONS ON robotics 2022年第1期38卷 180-196页

作者： Roehr, Thomas M. DFKI GmbH Robot Innovat Ctr D-28359 Bremen Germany

While traditional robotic systems come with a monolithic system design, reconfigurable multirobot systems can share and shift physical resources in an on-demand fashion. Multirobot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this article details an organization model as basis for planning with reconfigurable multirobot systems. The model allows us to exploit redundancies when optimizing a multirobot system's probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multirobot missions. We use a simulated multirobot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape. Our implementation of the organization model is open-source available (https://***/rock-knowledge-reasoning/knowledge-reasoning-moreorg).

关键词： Robots Multi-robot systems Organizations Planning Robot kinematics Redundancy Task analysis Multirobot systems planning reconfigurable robots scheduling and coordination space robotics and automation

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Extrinsic Calibration of Multiple Inertial Sensors From Arbitrary Trajectories

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IEEE robotics AND automation LETTERS 2022年第2期7卷 2055-2062页

作者： Lee, Jongwon Hanley, David Bretl, Timothy Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

We present a method of extrinsic calibration for a system of multiple inertial measurement units (IMUs) that estimates the relative pose of each IMU on a rigid body using only measurements from the IMUs themselves, without the need to prescribe the trajectory. Our method is based on solving a nonlinear least-squares problem that penalizes inconsistency between measurements from pairs of IMUs. We validate our method with experiments both in simulation and in hardware. In particular, we show that it meets or exceeds the performance-in terms of error, success rate, and computation time-of an existing, state-of-the-art method that does not rely only on IMU measurements and instead requires the use of a camera and a fiducial marker. We also show that the performance of our method is largely insensitive to the choice of trajectory along which IMU measurements are collected.

关键词： Calibration and identification sensor fusion space robotics and automation

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Hardware-Accelerated Mars Sample Localization Via Deep Transfer Learning From Photorealistic Simulations

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IEEE robotics AND automation LETTERS 2022年第4期7卷 12555-12561页

作者： Castilla-Arquillo, Raul Perez-del-Pulgar, Carlos Jesus Paz-Delgado, Gonzalo Gerdes, Levin Univ Malaga Dept Automat & Syst Engn Andalucia Tech Malaga 29070 Spain European Space Agcy Automat & Robot Sect NL-2201 Noordwijk Netherlands

The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation.

关键词： Deep learning for visual perception hardware-software integration in robotics RGB-D perception space robotics and automation transfer learning

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Visual Loop Closure Detection for a Future Mars Science Helicopter

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IEEE robotics AND automation LETTERS 2022年第4期7卷 12014-12021页

作者： Dietsche, Alexander Ott, Lionel Siegwart, Roland Brockers, Roland CALTECH Jet Prop Lab Pasadena CA 91109 USA Swiss Fed Inst Technol Autonomous Syst Lab CH-8092 Zurich Switzerland

Future Mars Rotorcrafts will require the ability to precisely navigate to previously visited locations in order to return to a safe landing site or execute precise scientific measurements, such as sample acquisition or targeted sensing. To enable a future Mars Science Helicopter to perform in-flight loop closures, we present an on-board visual loop closure detection system based on a Bag-of-Words (BoW) approach that is efficient enough to run in real-time on the anticipated computationally constrained flight avionics. Our system establishes image-to-image associations between incoming images of a downward-looking navigation camera and previously observed geo-tagged keyframes stored in a database. The system extracts ORB features which are quantized into a BoW histogram using a custom one million words hierarchical vocabulary, trained on synthetic images from a Mars simulation. An efficient database query using an inverted index produces a set of candidate frames which we check for geometrical consistency. For efficient feature matching, we leverage the vocabulary to perform fast approximate nearest neighbor search. The geometrical check accepts loop closure pairs whose essential matrix is supported by a minimum number of feature matches, which are pre-selected based on a rotational consistency check. The vocabulary and the methods used for the geometrical consistency checks were chosen to maximize the performance while allowing single-thread real-time execution on a computationally constrained embedded processor. We demonstrate and evaluate the proposed system both on simulated and real-world data, including flight data from the Mars Helicopter Ingenuity.

关键词： Aerial Systems Perception and Autonomy space robotics and automation

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On the Design, Modeling and Experimental Verification of a Floating Satellite Platform

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IEEE robotics AND automation LETTERS 2022年第2期7卷 1364-1371页

作者： Banerjee, Avijit Satpute, Sumeet G. Kanellakis, Christoforos Tevetzidis, Ilias Haluska, Jakub Bodin, Per Nikolakopoulos, George Lulea Univ Technol Dept Comp Elect & Space Engn Robot & AI S-97187 Lulea Sweden OHB Sweden AB S-16440 Stockholm Sweden

In this letter, a floating robotic emulation platform is presented with an autonomous maneuverability for a virtual demonstration of a satellite motion. Such a robotic platform design is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface. The design of the robotic platform, integrated with the sensor and actuator units, is briefly described, including the related component specification along with the mathematical model, describing its dynamic motion. Additionally, the article establishes a nonlinear optimal control architecture consisting of a unified model predictive approach for the overall manoeuvre tracking. The efficacy of the proposed modeling and control scheme is demonstrated in multiple experimental studies, where it is depicted that the proposed controller has the potential to address a precise point-to-point manoeuvre with terminal objectives, as well as an excellent path following capability. The proposed design is validated with extensive experimental studies, and it is supported with related results.

关键词： space robotics and automation optimization and optimal control nonlinear model predictive control friction-less floating platform satellite simulator

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Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning

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IEEE TRANSACTIONS ON robotics 2022年第1期38卷 317-328页

作者： Rudin, Nikita Kolvenbach, Hendrik Tsounis, Vassilios Hutter, Marco Swiss Fed Inst Technol Robot Syst Lab CH-8092 Zurich Switzerland

In this article, we show that learned policies can be applied to solve legged locomotion control tasks with extensive flight phases, such as those encountered in space exploration. Using an off-the-shelf deep reinforcement learning algorithm, we train a neural network to control a jumping quadruped robot while solely using its limbs for attitude control. We present tasks of increasing complexity leading to a combination of 3-D (re)orientation and landing locomotion behaviors of a quadruped robot traversing simulated low-gravity celestial bodies. We show that our approach easily generalizes across these tasks and successfully trains policies for each case. Using sim-to-real transfer, we deploy trained policies in the real world on the spaceBok robot placed on an experimental testbed designed for 2-D microgravity experiments. The experimental results demonstrate that repetitive controlled jumping and landing with natural agility is possible.

关键词： Robots Legged locomotion Task analysis Actuators Gravity Training Kinematics Deep learning in robotics and automation legged robots space robotics and automation

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Active Traversability Learning via Risk-Aware Information Gathering for Planetary Exploration Rovers

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IEEE robotics AND automation LETTERS 2022年第4期7卷 11855-11862页

作者： Endo, Masafumi Ishigami, Genya Keio Univ Dept Mech Engn Space Robot Grp Yokohama Kanagawa 2238522 Japan

Traversability prediction enables safe and efficient autonomous rover operation on deformable planetary surfaces. Revealing spatial distribution from terrain geometry to rover slip behavior is key to assessing prospective traversability, but is hindered by insufficient in situ measurements on hazardous states due to conservative rover traverses. To achieve a more accurate prediction, this letter proposes a framework that actively learns latent traversability by exploring informative terrain under the constraints of stochastic rover slip. With a Gaussian process (GP) modeling the spatial distribution, we devise an iterative two-stage framework that gradually refines the model estimation, combining risk-aware informative path planning and GP updates by taking in situ measurements. The path planning stage employs our designed sampling-based algorithm to generate informative trajectories with fault-tolerant risk inference, while the GP is cautiously updated with traverse data to avoid rover immobilization. Chance constraint formulation is exploited in the framework to infer the stochastic reachability of informative regions. Through GP estimates reducing uncertainty, the algorithm incrementally reaches informative yet hazardous states along feasible trajectories. Simulation studies in rough terrain environments demonstrate that the proposed framework gathers informative traverse data while averting rover stuck situations to estimate the latent traversability model.

关键词： Field robots motion and path planning probabilistic inference space robotics and automation

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