Robotic Exploration Research Articles

Feasibility of power beaming through the Venus atmosphere

Despite great interest by the planetary science community in the robotic exploration of Venus, solutions for operating in this extreme environment present significant challenges to mission architects. A particular challenge in designing any in situ Venus mission of reasonable duration is the selection of a suitable power source. State-of-the-art space power technologies comprising solar arrays, batteries and radioisotope thermoelectric generators are not capable of operating on the surface of Venus, limited by the high temperatures, high pressures and corrosive environment. Despite its proximity to the Sun, the solar resource on the surface is limited by the dense cloud layers. One potential approach for circumventing this limitation includes collecting the abundant visible solar energy above the Venus atmosphere, and wirelessly transferring this power to the surface at a wavelength that can more effectively penetrate the dense cloud layer. The feasibility of such an approach and other related mission concepts are discussed herein from a perspective of atmospheric absorption and scattering of the beamed energy.

Moon Diver: Exploring a pit's exposed strata to understand lunar volcanism

Natural pits on the Moon expose deep cross-sections of the lunar maria, enabling direct investigation of the Moon's volcanic history and providing potential access to subsurface lava tubes. The Moon Diver mission concept seeks to explore the Mare Tranquillitatis pit, which exposes the largest wall of bedrock of the mare pits (∼65 m). The concept is enabled by two innovative capabilities: pinpoint landing near the pit and robotic access to its near-vertical wall with an instrument package to examine the elemental chemistry, mineralogy, and morphology of these bedrock layers. Pinpoint landing uses closed-loop guidance with terrain-relative navigation (TRN), which was advanced by Perseverance landing on Mars, to deliver the lander within a 100-m ellipse. The Axel robotic explorer, which remains tethered to the lander, would egress onto the lunar surface and traverse the relatively flat terrain to the pit's funnel entrance. The lander, which is the data link to Earth, also serves as an anchor and provides power and communication to the rover through its tether. The rover is a novel two-wheeled platform with a trailing boom and a spool that pays out the tether as the rover traverses toward the pit. The 300-m long tether is well margined for the rover to scale the pit wall. The rover carries a surface preparation tool and three additional instrument types: (a) three high-resolution cameras for acquiring context images of the near and far walls with the near-wall pair in a stereoscopic configuration, (b) an alpha-particle-X-ray spectrometer (APXS) for elemental composition, and (c) a multi-spectral microscopic imager (MMI) that uses controlled lighting for mineralogy. The surface-preparation tool removes dust and patina from the rock wall by grinding a small area. This tool, the MMI, and the APXS would be deployed from an instrument bay inside the wheel wells. The rover would independently point each instrument at the same target on the wall with millimeter repeatability. Landing shortly after sunrise, the surface mission timeline is just shy of a lunar daytime (14 Earth days). Beyond the primary mission, the rover would be capable of descending from the overhang and peering into the void that may open to a large cave or lava tube. Lunar pits provide an exciting new target for exploration using innovative robotic capabilities that have been tested with integrated science instruments at multiple terrestrial analogue sites including a pit with basaltic layers in Arizona.

FADM-SLAM: a fast and accurate dynamic intelligent motion SLAM for autonomous robot exploration involving movable objects

PurposeMany popular simultaneous localization and mapping (SLAM) techniques have low accuracy, especially when localizing environments containing dynamically moving objects since their presence can potentially cause inaccurate data associations. To address this issue, the proposed FADM-SLAM system aims to improve the accuracy of SLAM techniques in environments containing dynamically moving objects. It uses a pipeline of feature-based approaches accompanied by sparse optical flow and multi-view geometry as constraints to achieve this goal.Design/methodology/approachFADM-SLAM, which works with monocular, stereo and RGB-D sensors, combines an instance segmentation network incorporating an intelligent motion detection strategy (iM) with an optical flow technique to improve location accuracy. The proposed AS-SLAM system comprises four principal modules, which are the optical flow mask and iM, the ego motion estimation, dynamic point detection and the feature-based extraction framework.FindingsExperiment results using the publicly available RGBD-Bonn data set indicate that FADM-SLAM outperforms established visual SLAM systems in highly dynamic conditions.Originality/valueIn summary, the first module generates the indication of dynamic objects by using the optical flow and iM with geometric-wise segmentation, which is then used by the second module to compute the starting point of a posture. The third module, meanwhile, first searches for the dynamic feature points in the environment, and second, eliminates them from further processing. An algorithm based on epipolar constraints is implemented to do this. In this way, only the static feature points are retained, which are then fed to the fourth module for extracting important features.

Online Learning of Neural Surface Light Fields Alongside Real-Time Incremental 3D Reconstruction

Immersive novel view generation is an important technology in the field of graphics and has recently also received attention for operator-based human-robot interaction. However, the involved training is time-consuming, and thus the current test scope is majorly on object capturing. This limits the usage of related models in the robotics community for 3D reconstruction since robots (1) usually only capture a very small range of view directions to surfaces that cause arbitrary predictions on unseen, novel direction, (2) requires real-time algorithms, and (3) work with growing scenes, e.g., in robotic exploration. The letter proposes a novel Neural Surface Light Fields model that copes with the small range of view directions while producing a good result in unseen directions. Exploiting recent encoding techniques, the training of our model is highly efficient. In addition, we design Multiple Asynchronous Neural Agents (MANA), a universal framework to learn each small region in parallel for large-scale growing scenes. Our model learns online the Neural Surface Light Fields (NSLF) aside from real-time 3D reconstruction with a sequential data stream as the shared input. In addition to online training, our model also provides real-time rendering after completing the data stream for visualization. We implement experiments using well-known RGBD indoor datasets, showing the high flexibility to embed our model into real-time 3D reconstruction and demonstrating high-fidelity view synthesis for these scenes. The code is available on github <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label>¹</label> <uri>https://jarrome.github.io/NSLF-OL</uri> </fn> .

Off-Policy Evaluation With Online Adaptation for Robot Exploration in Challenging Environments

Autonomous exploration has many important applications. However, classic information gain-based or frontier-based exploration only relies on the robot current state to determine the immediate exploration goal, which lacks the capability of predicting the value of future states and thus leads to inefficient exploration decisions. This letter presents a method to learn how “good” states are, measured by the state value function, to provide a guidance for robot exploration in real-world challenging environments. We formulate our work as an off-policy evaluation (OPE) problem for robot exploration (OPERE). It consists of offline Monte-Carlo training on real-world data and performs Temporal Difference (TD) online adaptation to optimize the trained value estimator. We also design an intrinsic reward function based on sensor information coverage to enable the robot to gain more information with sparse extrinsic rewards. Results show that our method enables the robot to predict the value of future states so as to better guide robot exploration. The proposed algorithm achieves better prediction and exploration performance compared with the state-of-the-arts. To the best of our knowledge, this work for the first time demonstrates value function prediction on real-world dataset for robot exploration in challenging subterranean and urban environments.

Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration

Autonomous robot operation in unstructured and unknown environments requires efficient techniques for mapping and exploration using streaming range and visual observations. Information-based exploration techniques, such as Cauchy–Schwarz quadratic mutual information and fast Shannon mutual information, have successfully achieved active binary occupancy mapping with range measurements. However, as we envision robots performing complex tasks specified with semantically meaningful concepts, it is necessary to capture semantics in the measurements, map representation, and exploration objective. This work presents semantic octree mapping and Shannon mutual information computation for robot exploration. We develop a Bayesian multiclass mapping algorithm based on an octree data structure, where each voxel maintains a categorical distribution over semantic classes. We derive a closed-form efficiently computable lower bound of the Shannon mutual information between a multiclass octomap and a set of range-category measurements using semantic run-length encoding of the sensor rays. The bound allows rapid evaluation of many potential robot trajectories for autonomous exploration and mapping. We compare our method against state-of-the-art exploration techniques and apply it in a variety of simulated and real-world experiments.

Light-driven dandelion-inspired microfliers

In nature, many plants have evolved diverse flight mechanisms to disperse seeds by wind and propagate their genetic information. Inspired by the flight mechanism of the dandelion seeds, we demonstrate light-driven dandelion-inspired microfliers based on ultralight and super-sensitive tubular-shaped bimorph soft actuator. Like dandelion seeds in nature, the falling velocity of the as-proposed microflier in air can be facilely controlled by tailoring the degree of deformation of the “pappus” under different light irradiations. Importantly, the resulting microflier is able to achieve a mid-air flight above a light source with a sustained flight time of ~8.9 s and a maximum flight height of ~350 mm thanks to the unique dandelion-like 3D structures. Unexpectedly, the resulting microflier is found to exhibit light-driven upward flight accompanied by autorotating motion, and the rotation mode can be customized in either a clockwise or counterclockwise direction by engineering the shape programmability of bimorph soft actuator films. The research disclosed herein can offer new insights into the development of untethered and energy-efficient artificial aerial vehicles that are of paramount significance for many applications from environmental monitoring and wireless communication to future solar sail and robotic spacecraft.

Evaluating UHMWPE-stuffed aluminium foam sandwich panels for protecting spacecraft against micrometeoroid and orbital debris impact

Robotic spacecraft shielding for protection against micrometeoroid impact is typically provided by existing structural panels, thermal insulation blankets, or reinforced component containers. Here the performance of an open cell aluminium foam core sandwich panel with intermediate ultra-high molecular weight polyethylene (UHMWPE) stuffing under hypervelocity impact of spherical aluminium projectiles was evaluated. Providing comparable structural and thermal performance as honeycomb sandwich panels, foam core sandwich panels have demonstrated improved impact shielding behaviour in existing studies. The inclusion of UHMWPE stuffing is found, through experimental testing and a comprehensive numerical study, to increase the shielding capability of foam core sandwich panels (40–80% increase in critical projectile diameter) for a modest weight penalty (25%). Compared to conventional aluminium honeycomb sandwich panels with equivalent weight, the improvement in shielding performance is >100%.

A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning.

In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.

Pre-launch lessons learned from NASA’S deep space network support for the artemis I mission to the moon

NASA's Deep Space Network (DSN) serves as a critical element in the exploration of deep space, providing telecommunication services such as tracking, telemetry, command, and delivery of science data to its customers around the globe. In addition to supporting robotic spacecraft for scientific missions, the DSN also supports human spaceflight (HSF) endeavors, including NASA's flagship Artemis missions to the Moon. This paper describes DSN lessons learned during preparations for the first Artemis mission, Artemis I. Topics covered include DSN performance and reliability, network loading, interface management, international collaboration and the role of international partners. Lessons learned will be applied to future missions, including Artemis II and beyond.

Visual Episodic Memory-based Exploration

In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is episodic memory which enables both the recollection of events from the past and the projection of subjective future. This paper explores the use of visual episodic memory as a source of intrinsic motivation for robotic exploration problems. Using a convolutional recurrent neural network autoencoder, the agent learns an efficient representation for spatiotemporal features such that accurate sequence prediction can only happen once spatiotemporal features have been learned. Structural similarity between ground truth and autoencoder generated images is used as an intrinsic motivation signal to guide exploration. Our proposed episodic memory model also implicitly accounts for the agent's actions, motivating the robot to seek new interactive experiences rather than just areas that are visually dissimilar. When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.

Simulating Operational Concepts for Autonomous Robotic Space Exploration Systems: A Framework for Early Design Validation

During mission design, the concept of operations (ConOps) describes how the system operates during various life cycle phases to meet stakeholder expectations. ConOps is sometimes declined in a simple evaluation of the power consumption or data generation per mode. Different operational timelines are typically developed based on expert knowledge. This approach is robust when designing an automated system or a system with a low level of autonomy. However, when studying highly autonomous systems, designers may be interested in understanding how the system would react in an operational scenario when provided with knowledge about its actions and operational environment. These considerations can help verify and validate the proposed ConOps architecture, highlight shortcomings in both physical and functional design, and help better formulate detailed requirements. Hence, this study aims to provide a framework for the simulation and validation of operational scenarios for autonomous robotic space exploration systems during the preliminary design phases. This study extends current efforts in autonomy technology for planetary systems by focusing on testing their operability and assessing their performances in different scenarios early in the design process. The framework uses Model-Based Systems Engineering (MBSE) as the knowledge base for the studied system and its operations. It then leverages a Markov Decision Process (MDP) to simulate a set of system operations in a relevant scenario. It then outputs a feasible plan with the associated variation of a set of considered resources as step functions. This method was applied to simulate the operations of a small rover exploring an unknown environment to observe and sample a set of targets.

Assessing the Usability of Two Declarative Programming Languages to Model Geometric Events

When space missions plan scientific actions for robotic spacecraft to execute, they frequently do so within a geometric context called an opportunity. Although there are geometric software libraries that let users write code to search for opportunities, they require knowledge of algorithms and imperative programming languages, which is a condition that might exclude a potentially large population of scientists. Additionally, there might be more user-friendly software systems for scientists to model and search for opportunities, but those might exclude other missions due to export concerns or an inability to maintain such software due to lack of staff or funding. To address these concerns, we designed two different computer languages to model opportunities. In this paper, we present these two languages, our study to evaluate their relative readability and usability, and results obtained in our research along with an interpretation of the same. The metric for this study has been a questionnaire with active exercises, statements with corresponding responses on a Likert scale, and open-ended questions to elicit qualitative responses. The study’s quantitative results provide us with relative and absolute quantification of the usability and readability of each language, while the study’s qualitative results help us direct future language design decisions.

Comparison of Learning Spacecraft Path-Planning Solutions from Imitation in Three-Body Dynamics

Spacecraft path-planning approaches that are capable of producing not only autonomous but also generalized solutions promise to open new modalities for robotic exploration and servicing in orbit environments characterized by epistemic uncertainty. Our hypothesis is that, in face of large systemic uncertainties, learning control policies from human demonstrations may offer a pathway to spacecraft guidance solutions that are more generalizable and complementary of baseline-dependent methods. In this context, the performance discrepancy between spacecraft path-planning policies trained from human demonstrations and optimal baseline solutions has not been characterized. We define a low-thrust, minimum-time transfer problem with predefined boundary constraints, across a wide range of binary asteroid systems, which form a “sandbox” environment for complex, highly variable, and poorly known orbit environments. Trajectories generated using optimal control theory (both indirect and direct methods) and human demonstrations (from a gamified version of the baseline environment) are collected. We test different implementations of behavioral cloning to analyze both feed forward and stateless long short-term memory regressions. As a result, we collect initial empirical evidence of performance degradation during cloning (compared to precisely optimal behavior) and performance stability across variations of the environment parameters. Such evidence informs the selection and improvement of the behavioral cloning architecture in future studies.

Time-Optimal Spiral Trajectories With Closed-Form Solutions

The Archimedean spiral is space-filling plane curve that is found in applications ranging from coverage path planning for robot exploration to scan pattern generation for medical imaging. The constant linear velocity (CLV) parameterization of this spiral is of particular interest due to its fixed path velocity and isotropic sampling capability, but the high accelerations near its origin singularity yield poor trajectory tracking that limit its utility. Here, I derive a closed-form time-optimal time scaling for CLV spirals with large path velocities that mitigates the singularity by inspecting the CLV spiral's acceleration envelope. When applied to two degree-of-freedom Cartesian scanner, I demonstrate that this approach reduces trajectory tracking error by up to 97.1% as compared to naïve CLV spirals with low computational overhead. I further show that this time scaling eliminates the central image distortion near the origin for scanning applications that rely on CLV spirals.

Editorial: Advancing robotic exploration of asteroids and comets: A threat, an opportunity, or both?

EDITORIAL article Front. Space Technol., 22 March 2023Sec. Space Robotics Volume 4 - 2023 | https://doi.org/10.3389/frspt.2023.1173593

질량 분석기의 원형 모델 개발

The mass spectrometer, being an essential scientific instrument for uncovering the origin of the solar system and life, has been used since the early 1970s on board spacecraft to obtain information of neutral and ionized elements in the atmosphere and surface of the moon, planets, asteroids, and comets. According to the 4th Basic Plan for the Promotion of Space Development (2023–2027), Korea plans to conduct lunar landing in 2032 and Mars landing in 2045 as the core goals of the plan and focuses on developing the technologies required for unmanned robotic exploration missions. In this regard, it is crucial to develop the technology of a mass spectrometer, which is the most fundamental payload for space exploration for maximized scientific achievements, however never tried before in any domestic space missions. We describe in this paper the principle of a domestically developed quadrupole mass spectrometer, its prototype model, and the test results of its performance. We conclude this paper with intended future improvements.

Robotic exploration of sub-ice shelf melting and freezing processes

Robotic exploration of sub-ice shelf melting and freezing processes

E-Sail Optimal Trajectories to Heliostationary Points

The aim of this paper is to investigate the performance of a robotic spacecraft, whose primary propulsion system is an electric solar wind sail (E-sail), in a mission to a heliostationary point (HP)—that is, a static equilibrium point in a heliocentric and inertial reference frame. A spacecraft placed at a given HP with zero inertial velocity maintains that heliocentric position provided the on-board thrust is able to counterbalance the Sun’s gravitational force. Due to the finite amount of storable propellant mass, a prolonged mission toward an HP may be considered as a typical application of a propellantless propulsion system. In this respect, previous research has been concentrated on the capability of high-performance (photonic) solar sails to reach and maintain such a static equilibrium condition. However, in the case of a solar-sail-based spacecraft, an HP mission requires a sail design with propulsive characteristics that are well beyond the capability of current or near-future technology. This paper shows that a medium-performance E-sail is able to offer a viable alternative to the use of photonic solar sails. To that end, we discuss a typical HP mission from an optimal viewpoint, by looking for the minimum time trajectory necessary for a spacecraft to reach a given HP. In particular, both two- and three-dimensional scenarios are considered, and the time-optimal mission performance is analyzed parametrically as a function of the HP heliocentric position. The paper also illustrates a potential mission application involving the observation of the Sun’s poles from such a static inertial position.

Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty

In this article, we present generalized polynomial chaos-based sequential convex programming (gPC-SCP) to compute a suboptimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control (SNOC) problem. The approach enables motion planning for robotic systems under uncertainty. The gPC-SCP method involves two steps. The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming for trajectory generation and control. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC. We also present the predictor–corrector extension (gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> ) for real-time motion trajectory generation in the presence of stochastic uncertainty. In the gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> method, we first predict the uncertainty using the gPC method and then optimize the motion plan to accommodate the uncertainty. We empirically demonstrate the efficacy of the gPC-SCP and the gPC-SCP <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\text{PC}$</tex-math></inline-formula> methods for the following two test cases: first, collision checking under uncertainty in actuation and physical parameters and second, collision checking with stochastic obstacle model for 3DOF and 6DOF robotic systems. We validate the effectiveness of the gPC-SCP method on the 3DOF robotic spacecraft testbed.