Search Results (66)

Future planetary exploration missions will rely heavily on efficient human–robot interaction to ensure astronaut safety and maximize scientific return. In this context, digital twins offer a promising tool for planning, simulating, and optimizing extravehicular activities. This study presents the development and evaluation of a digital twin for the AMADEE-24 analog Mars mission, organized by the Austrian Space Forum and conducted in Armenia in March 2024. Alternative local positioning methods were evaluated to enhance the system’s utility in Global Navigation Satellite System (GNSS)-denied environments. The digital twin integrates telemetry from the Aouda space suit simulators, inertial measurement unit motion capture (IMU-MoCap), and sensor data from the Intuitive Rover Operation and Collecting Samples (iROCS) rover. All nine experiment runs were reconstructed successfully by the developed digital twin. A comparative analysis of localization methods found that Simultaneous Localization and Mapping (SLAM)-based rover positioning and IMU-MoCap localization of the astronaut matched Global Positioning System (GPS) performance. Adaptive Cluster Detection showed significantly higher deviations compared to the previous GNSS alternatives. However, the IMU-MoCap method was limited by discontinuous segment-wise measurements, which required intermittent GPS recalibration. Despite these limitations, the results highlight the potential of alternative localization techniques for digital twin integration. Full article

The robotic exploration of space began only five decades ago, and yet in the intervening years, a wide and diverse ecosystem of robotic explorers has been developed for this purpose. Such devices have greatly benefited from miniaturization trends and the increased availability of high-quality commercial off-the-shelf (COTS) components. This review outlines the specific taxonomic distinction between planetary surface rovers and other robotic space exploration vehicles, such as orbiters and landers. Additionally, arguments are made to standardize the classification of planetary rovers by mass into categories similar to those used for orbital satellites. Discussions about recent noteworthy trends toward the miniaturization of planetary rovers are also included, as well as a compilation of previous planetary rovers. This analysis compiles relevant metrics such as the mass, the distance traveled, and the locomotion or actuation technique for previous planetary rovers. Additional details are also examined about archetypal rovers that were chosen as representatives of specific small-scale rover classes. Finally, potential future trends for miniature planetary surface rovers are examined by way of comparison to similar miniaturized orbital robotic explorers known as CubeSats. Based on the existing relationship between CubeSats and their Earth-based simulation equivalents, CanSats, the importance of a potential Earth-based analog for miniature rovers is identified. This research establishes such a device, coining the new term ‘CanBot’ to refer to pathfinding systems that are deployed terrestrially to help develop future planetary surface exploration robots. Establishing this explicit genre of robotic vehicle is intended to provide a unified means for categorizing and encouraging the development of future small-scale rovers. Full article

Extraterrestrial exploration presents unique challenges for robotic systems, as traditional rigid rovers face limitations in stowage volume, traction on unpredictable terrain, and susceptibility to damage. Soft robotics offers promising solutions through bio-inspired designs that can mimic natural locomotion mechanisms. Here, we present an optimised, spider-inspired soft jumping robot for extraterrestrial exploration that addresses key challenges in soft robotics: actuation efficiency, controllability, and deployment. Drawing inspiration from spider physiology—particularly their hydraulic extension mechanism—we develop a lightweight limb capable of multi-modal behaviour with significantly reduced energy requirements. Our 3D-printed soft actuator leverages pressure-driven collapse for efficient retraction and pressure-enhanced rapid extension, achieving a power-to-weight ratio of 249 W/kg. The integration of a non-backdriveable clutch mechanism enables the system to hold positions with zero energy expenditure—a critical feature for space applications. Experimental characterisation and a subsequent optimisation methodology across various materials, dimensions, and pressures reveal that the robot can achieve jumping heights of up to 1.86 times its body length. The collapsible nature of the soft limb enables efficient stowage during spacecraft transit, while the integrated pumping system facilitates self-deployment upon arrival. This work demonstrates how biologically inspired design principles can be effectively applied to develop versatile robotic systems optimised for the unique constraints of extraterrestrial exploration. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

This paper presents a novel framework addressing the fundamental challenge of concurrent real-time audio acquisition and motor control in resource-constrained mobile robotics. The ESP32-based system integrates a digital MEMS microphone with rover mobility through a unified Bluetooth protocol. Key innovations include (1) a dual-thread architecture enabling non-blocking concurrent operation, (2) an adaptive eight-bit compression algorithm optimizing bandwidth while preserving audio quality, and (3) a mathematical model for real-time resource allocation. A comprehensive empirical evaluation demonstrates consistent control latency below 150 ms with 90–95% audio packet delivery rates across varied environments. The framework enables mobile acoustic sensing applications while maintaining responsive motor control, validated through comprehensive testing in 40–85 dB acoustic environments at distances up to 10 m. A performance analysis demonstrates the feasibility of high-fidelity mobile acoustic sensing on embedded platforms, opening new possibilities for environmental monitoring, surveillance, and autonomous acoustic exploration systems. Full article

Defect repair on custom-curved glulam beams is still performed manually because knots are irregular, numerous, and located on elements that cannot pass through linear production lines, limiting the scalability of timber-based architecture. This study presents Woodot, an autonomous mobile robotic platform that combines an omnidirectional rover, a six-dof collaborative arm, and a fine-tuned Segment Anything computer vision pipeline to identify, mill, and plug surface knots on geometrically variable beams. The perception model was trained on a purpose-built micro-dataset and reached an F1 score of 0.69 on independent test images, while the integrated system located defects with a 4.3 mm mean positional error. Full repair cycles averaged 74 s per knot, reducing processing time by more than 60% compared with skilled manual operations, and achieved flush plug placement in 87% of trials. These outcomes demonstrate that a lightweight AI model coupled with mobile manipulation can deliver reliable, shop-floor automation for low-volume, high-variation timber production. By shortening cycle times and lowering worker exposure to repetitive tasks, Woodot offers a viable pathway to enhance the environmental, economic, and social sustainability of digital timber construction. Nevertheless, some limitations remain, such as dependency on stable lighting conditions for optimal vision performance and the need for tool calibration checks. Full article

Path-planning algorithms for planetary rovers are critical for autonomous robotic exploration, enabling the efficient and safe traversal of complex and dynamic extraterrestrial terrains. Unlike terrestrial mobile robots, planetary rovers must navigate highly unpredictable environments influenced by diverse factors such as terrain variability, obstacles, illumination conditions, and temperature fluctuations, necessitating advanced path-planning strategies to ensure mission success. This review comprehensively synthesizes recent advancements in planetary rover path-planning algorithms. First, we categorize these algorithms from a constraint-oriented perspective, distinguishing between internal rover state constraints and external environmental constraints. Next, we examine rule-based path-planning approaches, including graph search-based methods, potential field methods, sampling-based techniques, and dynamic window approaches, analyzing representative algorithms in each category. Subsequently, we explore bio-inspired path-planning methods, such as evolutionary algorithms, fuzzy computing, and machine learning-based approaches, with a particular emphasis on the latest developments and prospects of machine learning techniques in planetary rover navigation. Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions. Full article

This paper aims to analyze a six-wheeled mobile manipulator as a solution for brush clearing difficult areas. To this end, a rover with a rocker–bogie suspension system, like those used for space explorations, is considered; the cutting head is moved by a robotic arm fixed to the rover so that it can reach areas to clean in front of the rover or on its sides. The change of the pose of the robotic arm shifts the centre of mass of the rover and, although the shift is not important, it can be used to improve stability, to overcome an obstacle, or to change the load distribution between the wheels to prevent the wheels from slipping or sinking. Some analyses of the interaction between the rover and robotic arm are reported in this paper. To prevent the rover from entering a low-grip area, the possibility of estimating the grip conditions of the terrain is considered, using the front wheels as tactile sensors. By keeping the rear wheels stationary and gradually increasing the torque on the front wheels, it is possible to evaluate the conditions under which slippage occurs. In case of poor grip, using the other drive wheels, the rover can reverse its direction and look for an alternative path. Full article

This study presents a deep learning-based prediction system with an elevated approach to prevent tip-over incidents on planetary exploration rovers, enhancing their operational safety and reliability. Planetary rovers, critical for space exploration missions, must navigate through uneven surfaces and terrains with undefined interaction properties. Future planetary rovers must navigate harsher terrains, like steep craters and caves, to access critical scientific data, significantly risking tip-over in any state of operational control. The proposed system employs linear accelerations and angular velocities measured by the accelerometer and the gyroscope of the Inertial Measurements Unit (IMU) to monitor the rover’s dynamic behavior and stability while navigating the environment. By leveraging deep learning algorithms, the system evaluates predictions and true measurements in real time to identify potential tip-overs. Additionally, the system provides the possibility to adjust the rover’s motion to prevent failure. The efficacy of this prediction-based approach is validated through simulations and field tests on two robotic platforms, the Asguard v4 and Coyote 3 rovers, demonstrating its capability to reduce the incidence of tip-overs under various challenging conditions. The integration of this system aims to extend the operational lifespan of rovers, optimize mission outcomes, and enhance the overall safety of planetary exploration missions. Full article

Mobile robots are capable of moving in various environments and performing complex tasks. They are essential in applications such as planetary exploration, search missions, hazardous waste cleanup, and process automation. Therefore, their study and improvement are relevant today. In this research, we propose optimizing the rocker–bogie suspension system to enhance the robustness of an autonomous rover used in precision agriculture (PA). PA aims to maximize agricultural efficiency and productivity through advanced technologies, and autonomous rovers play a crucial role in enabling real-time data collection and decision-making. This work was developed by implementing numerical simulations to evaluate the performance of the suspension system. The rocker–bogie suspension system is widely used in space exploration as it can avoid obstacles and maintain stability in challenging terrain. Using degrees of freedom and structural analysis, we designed and validated a rocker–bogie-type suspension geometry adapted to the needs of PA. The results of the simulations showed that optimizing the rocker–bogie suspension system significantly improves the rover’s robustness on uneven surfaces. The performance of the system was evaluated in various scenarios and conditions through numerical simulations, which supported its feasibility and effectiveness in PA. In conclusion, optimizing the rocker–bogie suspension system is an effective strategy to enhance the robustness of an autonomous rover in PA, as demonstrated by the results of the static simulations. This finding has significant implications for maximizing efficiency and agricultural productivity in PA. Full article

This paper demonstrates the performance of autonomous rovers utilizing NeuroEvolution of Augmenting Topologies (NEAT) in multi-room scenarios and explores their potential applications in wildfire management and search and rescue missions. Simulations in three- and four-room scenarios were conducted over 100 to 10,000 generations, comparing standard learning with transfer learning from a pre-trained single-room model. The task required rovers to visit all rooms before returning to the starting point. Performance metrics included fitness score, successful room visits, and return rates. The results revealed significant improvements in rover performance across generations for both scenarios, with transfer learning providing substantial advantages, particularly in early generations. Transfer learning achieved 32 successful returns after 10,000 generations for the three-room scenario compared to 34 with standard learning. In the four-room scenario, transfer learning achieved 32 successful returns. Heatmap analyses highlighted efficient navigation strategies, particularly around starting points and target zones. This study highlights NEAT’s adaptability to complex navigation problems, showcasing the utility of transfer learning. Additionally, it proposes the integration of NEAT with UAV systems and collaborative robotic frameworks for fire suppression, fuel characterization, and dynamic fire boundary detection, further strengthening its role in real-world emergency management. Full article

Exploration rovers have difficulty moving underground because the drag force from the ground restricts their movement; this hinders underground exploration. This study aimed to address this challenge. We posit a hypothesis that the rover can move underground by imparting vibration to the ground and changing the drag force. To validate this hypothesis, a testbed that moves underground was developed, and the drag force when imparting vibration was investigated. The results revealed that the drag force while imparting vibration is smaller than that after imparting vibration, and we accordingly devised the operation for moving underground. The proposed operation causes bias of the drag force by imparting vibration to make the testbed move in the direction of the small drag force. The effectiveness of the proposed method was assessed through an experiment wherein the testbed was set to move underground. The experimental results demonstrate the superiority of the proposed method, as the movement distance achieved with vibration is considerably greater than that without vibration. The findings validate the hypothesis that using vibration for underground motion is effective in improving mobility and provides valuable insights into the design of robots for underground motion. Full article

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team’s mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover’s location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover’s localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations. Full article

EELS-DARTS is a simulator designed for autonomy development and analysis of large degree of freedom snake-like robots for space exploration. A detailed description of the EELS-DARTS simulator design is presented. This includes the versatile underlying multibody dynamics representation used to model a variety of distinct snake robot configurations as well as an anisotropic friction model for describing screw–ice interaction. Additional simulation components such as graphics, importable terrain, joint controllers, and perception are discussed. Methods for setting up and running simulations are discussed, including how a snake robot’s autonomy stack closes the commands and information loop with the simulation via ROS. Multiple use cases are described to illustrate how the simulation is used to aid and inform robot design, autonomy development, and field test use throughout the project’s life cycle. A validation analysis of the screw–ice contact model is performed for the surface mobility case. Lastly, an overview of simulation use for planning operations during a recent field test to the Athabasca Glacier in Canada is discussed. Full article

In the current scenario, renewable energy resources play a vital role in developing countries. Photovoltaic panel-based power plants have great significance for the production of electrical energy, due to automated transitions, easy interaction, and low maintenance costs. The energy-production efficiency depends on various factors and environmental effects. The formulation of a thin layer of dust particles on solar panels diminishes their energy-production capacity due to the limiting of heat transfer to the semiconducting materials. The current paper deals with the design and fabrication of an automated water-jet-assisted robot used to clean the dust particle layer and bird droppings on PV panels. In the water-jet-assisted cleaning robot, there is a two-brush mechanism that works in concert with the rover’s movement. A Bluetooth communication system plays a vital role by controlling the rover; this approach offers a perspective on a rover constructed with an Arduino-UNO micro-controller, along with an HC-05 Bluetooth sensor. Full article