Robotic Exploration Research Articles

On the Interplanetary Cosmic-Space Temperature Fluctuations Application Possibility with Superconductors and Molecular Effect Modelling for Energy Optimization in Future Space Long-Voyages

Superconductors Molecular Effect Model (MEM) was published in a series of contributions for High Temperature Superconductors (HTSC) Type I and II. A cautious pre-hypothesis is presented for future applications with these HTSC for optimizing the exploitation of cosmic space temperature fluctuations, not exclusively related to MEM. This pre-hypothesis project for possible upcoming missions applications, both in human-crew or robotic spacecrafts is explained and technically justified. Advantages and inconvenients are discussed. A review of 3D Interior and Graphical Optimization with HTSC MEM model, namely Hg-Cuprates, is included with main Critical Temperature numerical algorithms/formulas previously developed. This updated refinement for Hg-Cuprates MEM shows error in TC prediction ≈ 4 K, and is the forward improvement whose errors have to be continued decreasing in next MEM development stages.

Optimized Frontier-Based Path Planning Using the TAD Algorithm for Efficient Autonomous Exploration

A novel path-planning method utilizing the trapezoid, adjacent, and distance, (TAD) characteristics of frontiers is presented in this work. The method uses the mobile robot’s sensor range to detect frontiers throughout each exploration cycle, modifying them at regular intervals to produce their parameters. This well-thought-out approach makes it possible to choose objective points carefully, guaranteeing seamless navigation. The effectiveness and applicability of the suggested approach with respect to exploration time and distance are demonstrated by empirical validation. Results from experiments show notable gains over earlier algorithms: time consumption decreases by 10% to 89% and overall path distance for full investigation decreases by 12% to 74%. These remarkable results demonstrate the efficacy of the suggested approach and represent a paradigm change in improving mobile robot exploration in uncharted territory. This research introduces a refined algorithm and paves the way for greater efficiency in autonomous robotic exploration. This study opens the door for more effective autonomous robotic exploration by introducing an improved algorithm.

Analyzing bearing capacity changes due to vibration in discrete element method simulations

Recently, legged robots have gained significant attention as highly mobile platforms for planetary exploration. However, the surfaces of celestial bodies such as the Moon are mainly composed of loose materials, leading to slippage due to the deformation of the surface under the movements of the rover’s legs. To address this issue, we proposed a walking method designed to minimize slippage. Our previous research demonstrated that applying vibrations can increase both the shear strength of the ground and the amount of the rover’s leg subsidence, thereby enhancing the ground’s bearing capacity, which is related to the counterforce provided by the ground against the legs of the rover. For the robot to perform optimally, it is essential to accurately estimate this bearing capacity to select efficient vibration settings. In this study, we utilized the discrete element method (DEM) to simulate the ground’s bearing capacity under various vibrational influences changing both the sinkage depth of a leg and the vibration frequency. Our simulations successfully mirrored the real-world effects of vibrations on bearing capacity, providing insightful perspectives on how vibration can be used to enhance ground support for these robotic explorers.

UUVDNet: An efficient unmanned underwater vehicle target detection network for multibeam forward-looking sonar

Precise localization of unmanned underwater vehicle (UUV) on multi-beam forward-looking sonar (MFLS) is a key technology in underwater robotic exploration. However, large appearance change and weak features of targets in MFLS image, pose a serious challenge on target detection. This paper proposes an efficient UUV detection model with an enhanced training scheme and convolutional block attention mechanism. The proposed training scheme is composed of dual-path gradient information optimization and adaptive loss adjustment, which improves the network’s detection capabilities and the underwater environment adaptability. The convolutional block attention module (CBAM) is developed for extracting channel and spatially weighted feature maps, and providing a mechanism to adaptive feature refinement. We construct a comprehensive sonar detection dataset from field experiments. By comparing existing state-of-the-art detection algorithms, our proposed UUVDNet outperforms Faster R-CNN, Cascade R-CNN, CenterNet, SSD, YOLOv8n, DPFIN, and MBSNN in terms of mAP50–95 by 39.5%, 35.7%, 8.4%, 14.0%, 2.5%, 13.6%, and 21.8%, respectively. Furthermore, our inference model size is the most compact among all detection models, with a size of 5.1M. Final ablation experiments effectively showcase the proposed components’ efficiency.

Space Economy, Tourism and Logistics

The advancements in technology that developed with the Industrial Revolution provided people with the opportunity to explore their surroundings. Especially, the rocket technology developed during World War II paved the way for space exploration. The launching of the first satellites by Russia and the USA in 1957-58 marked the beginning of understanding the unknown and uncertainty of space. With the initiation of space research by Russia and the USA, the magnitude of the space industry has been increasing day by day. In recent times, the space economy is around $240 billion, while space tourism is around $1.5 billion. Space has attracted the interest of both states and private companies. Particularly, after the 2000s, space activities have been commercialized by private companies. In this study, the space economy has been evaluated within the scope of space tourism and space logistics. Qualitative in-depth analysis and quantitative VOSviewer methods have been used together in the study. In recent times, with SpaceX's reusable rocket technology, Falcon 9, a revolution has taken place in transportation and logistics in space. Also, SpaceX's low-cost approach providing access to space at more affordable costs is a significant advantage for future crewed or robotic space missions. The increase in space research and travel to space, the Moon, Mars, and deep space in the future will increase the volume of space tourism and, generally, the space economy, primarily boosting the importance of space logistics as it forms the basis of this process.

A Conceptual Mission Architecture of a Robotic Spaceflight to Enceladus for Enabling Exploration of its Surface and Subglacial Ocean

Sixth-largest moon of Saturn Enceladus is one of the most promising destinations in the Solar System to search for possible presence of extraterrestrial life. The basis of such assumptions is the confirmed presence of a large accumulation of water around Enceladus South Pole under a layer of newly formed ice of 3 to 5 km thick. In the same region, many cryovolcanic sources have been observed, studying of which can provide understanding about development of life on Earth and searching for life on other planets in the Solar System. The paper presents a conceptual mission architecture to explore Saturn’s moon Enceladus with a possibility of conducting surface and subglacial ocean studies. Proposed mission scientific goals and objectives defined technical requirements and the framework of the project. The project includes developing an automatic interplanetary station for studying the surface of Enceladus and equipment required for a contact study on and under the surface, such as a landing platform, rover, melting plant and floating robotic systems. One of the key tasks of the mission development is a landing site selection. Since the terrain of Enceladus is unknown, the choice of the exact location must be made at the time of landing, if the lander can observe the surface. The landing system proposed in this work is a special composition of a platform landing equipment assembly, which includes a set of collapsible surfaces, fixed landing supports, control and braking systems with low-thrust engines designed within the framework of the project. Proposed in this study is a.subglacial underwater robotic vehicle designed for obtaining the most accurate and detailed information about the composition of the Enceladus Ocean water and a possibility of life there. A melting device powered by a radioisotope energy source melts the ice crust allowing the subglacial robotic vehicle to immerse into the ocean. Using contour heat pipes enables heat transfer to the ice and controls the melting process without conversion into electricity. The proposed mission concept can provide a more complete picture of Enceladus as a celestial body, give a detailed description of its surface, characteristics of the subglacial ocean, and potentially answer the question of the existence of life in the subglacial ocean of Enceladus. Keywords: Enceladus, Subglacial Exploration, Robotic Transplanetary Mission

A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

Abstract Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly–Exploring Random Trees* (BI–RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub–regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

Time-optimal ergodic search: Multiscale coverage in minimum time

Search and exploration capabilities are essential for robots to inspect hazardous areas, support scientific expeditions in extreme environments, and potentially save human lives in natural disasters. The variability of scale in these problems requires robots to reason about time alongside their dynamics and sensor capabilities to effectively assess and explore for information. Recent advances in ergodic search methods have shown promise in supporting trajectory planning for exploration in continuous, multiscale environments with dynamics consideration. However, these methods are still limited by their inability to effectively reason about and adapt the time to explore in response to their environment. This ability is crucial for adapting exploration to variable-resolution information-gathering tasks. To address this limitation, this paper poses the time-optimal ergodic search problem and investigates solutions for fast, multiscale, and adaptive robotic exploration trajectories. The problem is formulated as a minimum-time problem with an ergodic inequality constraint whose upper bound specifies the amount of coverage needed. We show the existence of optimal solutions using Pontryagin’s conditions of optimality, and we demonstrate effective, minimum-time coverage numerically through a direct transcription optimization approach. The efficacy of the approach in generating time-optimal search trajectories is demonstrated in simulation under several nonlinear dynamic constraints, and in a physical experiment using a drone in a cluttered environment. We find that constraints such as obstacle avoidance are readily integrated into our formulation, and we show through an ablation study the flexibility of search capabilities at various scales. Last, we contribute a receding-horizon formulation of time-optimal ergodic search for sensor-driven information-gathering and demonstrate improved adaptive sampling capabilities in localization tasks.

VE76 Emergency robotic exploration for obstructed parastomal hernia post robotic radical cystectomy with ileal conduit - our initial experience, tips and tricks

VE76 Emergency robotic exploration for obstructed parastomal hernia post robotic radical cystectomy with ileal conduit - our initial experience, tips and tricks

A Comparative Study on Human Space Flights over Robotic Space Missions and Habitation involved in the Human Space Mission

A Comparative Study on Human Space Flights over Robotic Space Missions and Habitation involved in the Human Space Mission

Online tree-based planning for active spacecraft fault estimation and collision avoidance.

Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot's state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST's convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST's performance.

Planning under uncertainty for safe robot exploration using Gaussian process prediction

The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.

The exploration of multifunctional liquid robotics with ferrofluids: Fabrication, control, operation and sensing

Liquid robotics with arbitrary deformability and rich functionality can adapt to various environmental constraints and perform many significant tasks. To turn this vision into reality, an intelligent liquid system, ferrofluid, which possesses excellent macroscopic and microscopic responsiveness to external magnetic fields, is explored as the rudiment of liquid robotics. Focus is given not only to its improved fabrication and cooperative programming control, but also its underexplored object manipulation and signal transmission. The prepared ferrofluid consists of sterically stabilized ultra-small magnetic nanoparticles suspended in carrier liquids, showing strong stability, flexible mobility, and substantial magnetization. Intelligent locomotion and complex morphology control are successfully achieved by a programmable platform that generates sequential pre-designed magnetic fields through the timing activation of specific electromagnets. Meanwhile, by utilizing the magneto-Archimedes effect, this ferrofluid system can perform multi-dimensional manipulation of objects with densities ranging from 1.1 to 8.9 g/cm3 in cooperation with varying magnetic fields. Furthermore, this system can sense vibrations due to its intrinsic liquid feature and send corresponding signals through detecting the changes in magnetic flux by external coils. Similarly, the flow, concentration, and speed of ferrofluids can also be detected. These advancements in fabrication, control, operation, and sensing are crucial for the development of liquid robotics.

An improved frontier-based robot exploration strategy combined with deep reinforcement learning

The map of the environment is the basis for autonomous robot navigation. This paper introduces an improved approach to frontier-based exploration by utilizing deep reinforcement learning to select target points. This study proposes a novel approach for map sampling and developing a corresponding neural network architecture. Our method aims to adapt effectively to unfamiliar environments with varying dimensions and diverse action spaces while reducing the loss of information caused by map sampling. We train and validate the neural network in a simulation environment. The results show that our proposed method can stably explore unknown environments of different sizes, while the distance traveled to complete the exploration is shorter than other methods. In addition, we conducted experiments on a real robot, and the results show that our method can be easily transferred from the simulation environment to the real environment.

Task-Driven Reinforcement Learning With Action Primitives for Long-Horizon Manipulation Skills.

It is an interesting open problem to enable robots to efficiently and effectively learn long-horizon manipulation skills. Motivated to augment robot learning via more effective exploration, this work develops task-driven reinforcement learning with action primitives (TRAPs), a new manipulation skill learning framework that augments standard reinforcement learning algorithms with formal methods and parameterized action space (PAS). In particular, TRAPs uses linear temporal logic (LTL) to specify complex manipulation skills. LTL progression, a semantics-preserving rewriting operation, is then used to decompose the training task at an abstract level, informs the robot about their current task progress, and guides them via reward functions. The PAS, a predefined library of heterogeneous action primitives, further improves the efficiency of robot exploration. We highlight that TRAPs augments the learning of manipulation skills in both learning efficiency and effectiveness (i.e., task constraints). Extensive empirical studies demonstrate that TRAPs outperforms most existing methods.

Robotic exploration of Martian caves: Evaluating operational concepts through analog experiments in lava tubes

Caves on Mars are a tantalizing target for exploration, as they could harbor evidence of extinct or extant life, clues to the planet’s geological history, and even potential future human habitation. However, the inherent challenges of navigating unknown and challenging terrain, coupled with limited communication capabilities, pose significant obstacles for robotic exploration in these caves. This paper presents the results of an effort to evaluate different operational concepts for a mission dedicated to exploration of caves on Mars. We conducted a series of analog exploration experiments in lava tubes on Earth, testing two hypotheses: (1) that two robots are more effective than one, and (2) that high levels of autonomy are more effective than low levels of autonomy. Our findings suggest that two robots are indeed more effective, except in low autonomy cases, where more operational resources are required. We also found that full autonomy is more efficient than low autonomy, as it enables quicker exploration and detection of targets of interest. However, the low autonomy cases benefited from the operator input to acquire higher quality data, an area of autonomy requiring further development. This paper provides insight into the design of our experiments, as well as detailing the results and implications for the design of future missions to explore caves on Mars. By shedding light on the operational concepts tested and their corresponding outcomes, we contribute to the knowledge base required to formulate optimal strategies for the realization of successful cave exploration missions on the Red Planet.

Development of a modular half-duplex frequency-agile X-band transceiver for CubeSats and robotic spacecraft

The EPFL Spacecraft Team (EST) is working on the Constellation of High-Performance Exospheric Science Satellites (CHESS) mission, which aims to launch a constellation of two CubeSats in 2027. These CubeSats will study the chemical composition of Earth’s exosphere using time-of-flight mass spectrometry. To downlink the collected scientific data, the satellites will utilise a custom X-band transceiver, presented in this paper, operating in the amateur frequency range of 10.45GHz to 10.50GHz. The proposed transceiver architecture employs Software-Defined Radio (SDR) technology and can be easily adapted for different use cases. This modular approach has the potential to be suitable for future spacecraft missions, such as active debris removal or lifetime extension services, where flexible communication systems are required.

CURE: A Hierarchical Framework for Multi-Robot Autonomous Exploration Inspired by Centroids of Unknown Regions

In this paper, a novel multi-robot autonomous exploration approach CURE is proposed based on dynamic Voronoi diagrams and centroids of unknown connected regions. Compared with existing approaches, the novelty of this work is twofold: 1) Dynamic Voronoi diagram is used for partition of the space being explored to improve the efficiency of multi-robot exploration, and then a new parameter-insensitive utility function is elaborately designed to evaluate the information of centroids, which helps guide the robot to explore unknown regions. 2) A hierarchical framework consisting of global and local exploration windows for detecting centroids is designed, wherein the global exploration window is activated to find centroids to guide the robot exploration when there are no centroids in any one local exploration window. We validate the feasibility and exploration efficiency of the proposed approach in various complex simulation scenarios and challenging real-world tasks. All test results show that the exploration time consumption and path cost are reduced by up to 50.7% and 34.4%, respectively, compared with an advanced RRT-based multi-robot exploration approach. (Supplementary video link: https://youtu.be/P5jXKlGQOec) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the efficient multi-robot autonomous exploration problem. In some applications such as target search and disaster rescue, the information about the environment is totally unknown to the robots, and thus they are required to explore unknown environments autonomously. In this case, it is necessary to improve the efficiency of multi-robot exploration due to the time limitation of the task and the battery capacity. In this paper, a hierarchical framework is proposed to improve the efficiency of multi-robot autonomous exploration. Each robot only needs to explore the Voronoi partition it is responsible for and is guided to the unknown region by the centroid detected in the global and local exploration windows. Overall, the proposed approach can dramatically reduce the exploration time and path cost.

Exploring beyond Earth using space robotics.

Robotic spacecraft enable exploration of our Solar System beyond our human presence. Although spacecraft have explored every planet in the Solar System, the frontiers of space robotics are at the cutting edge of landers, rovers, and now atmospheric explorers, where robotic spacecraft must interact intimately with their environment to explore beyond the reach of flyby and orbital remote sensing. Here, we describe the tremendous growth in space robotics missions in the past 7 years, with many new entities participating in missions to the surface of the Moon, Mars, and beyond. We also describe the recent development of aerial missions to planets and moons, as exemplified by the Ingenuity helicopter on Mars and the Dragonfly mission to Titan. We focus on suborbital robotics-landers, rovers, and aerial vehicles-with associated challenges in sensing, manipulation, mobility, and system-level autonomy.

Demonstration of charge-hold-vent (CHV) and no-vent-fill (NVF) in a simulated propellent storage tank during tank-to-tank cryogen transfer in microgravity

The space exploration from a low earth orbit to a high earth orbit, then to Moon, Mars, and possibly asteroids and moons of other planets is one of the biggest challenges for scientists and engineers for the new millennium. The enabling of in-space cryogenic rocket engines and the Lower-Earth-Orbit (LEO) cryogenic fuel depots for these future manned and robotic space exploration missions begins with the technology development of advanced cryogenic thermal-fluid management systems for the propellant transfer line and storage tank system. One of the key thermal-fluid management operations is the chilldown and filling of the propellant storage tank in space. As a result, highly energy efficient, breakthrough concepts for quenching heat transfer to conserve and minimize the cryogen consumption during chilldown have become the focus of engineering research and development, especially for the deep-space mission to Mars. In this paper, we introduce such thermal transport concepts and demonstrate their feasibilities in space for cryogenic propellant storage tank chilldown and filling in a simulated space microgravity condition on board an aircraft flying a parabolic trajectory. In order to maximize the storage tank chilldown thermal efficiency for the least amount of required cryogen consumption, the breakthrough quenching heat transfer concepts developed include the combination of charge-hold-vent (CHV) and no-vent-hold (NVF). The completed flight experiments successfully demonstrated the feasibility of the concepts and discovered that spray cooling combined with hold and vent is more efficient than the pure spray cooling for storage tank chilldown in microgravity. In microgravity, the data shows that the CHV thermal efficiency can reach 39.5%. The CHV efficiency in microgravity is 6.9% lower than that in terrestrial gravity. We also found that pulsing the spray can increase CHV efficiency by 6.1% in microgravity.