ABSTRACT Navigation within complex pipelines in sectors such as construction, mining, and underwater exploration presents significant demands on robotic adaptability and maneuverability. While traditional soft robots achieve such navigation through large material deformations, they face an inherent conflict between deformation capacity and structural rigidity, which hinders the simultaneous attainment of high traversability and strong load‐bearing capacity. To address this limitation, we propose a novel structural framework to overcome existing constraints of pipeline robots through integrating the characteristics of auxetic metamaterials. Firstly, the proposed robot is monolithically fabricated via 3D printing techniques. The mechanical properties of the printing material are characterized through quasi‐static tensile tests. Subsequently, the deformation behavior of the robotic body is analyzed and compared between numerical and experimental methods. Ultimately, the robot's navigation through complex pipelines is simulated and compared with physical pipe‐passing tests. The results demonstrate that during navigation in complex pipelines, the auxetic structure of the robot deforms radially, which releases circumferential pressures. This pressure release thereby reduces motion resistance, enhancing the robot's overall maneuverability. The findings of this work transcend the performance limitations of rigid materials, thereby offering a viable pathway for robotic navigation within the complex piping networks.
ABSTRACT A cooperative‐navigation algorithm based on an extended Kalman filter (EKF) is proposed for Leader–Follower autonomous underwater vehicles (AUVs) to address inconsistencies in inter‐vehicle navigation coordinate systems caused by the absence of geo‐referencing signals in anchor‐free environments. The algorithm integrates inertial compensation and a dynamic ranging threshold to address challenges posed by low update rates (0.05–0.07 Hz), high latency, time‐varying hydroacoustic channels, and multipath interference in hydroacoustic communication, all of which significantly degrade navigation accuracy. To mitigate latency from low‐rate hydroacoustic links, we use time‐tag alignment (UTC‐referenced clock synchronization) to propagate the Leader state to the effective ranging time for inertial latency compensation. In response to the hydroacoustic channel variability and multipath interference, a dynamic ranging threshold based on the AUV's motion capabilities is proposed to eliminate outlier ranging data. To enhance the robustness of cooperative navigation and maintain a coordinate system, a velocity‐bounded constraint is introduced into the EKF framework, limiting the magnitude of individual position corrections. During a field test at Danjiangkou Reservoir, Henan Province, China, AUVs cooperatively mapped an underwater terrain area of at a water depth of 45–55 m. Compared with terrain mapping using a single‐AUV inertial navigation system, the proposed cooperative‐navigation method improved the agreement of the terrain mapping with a Global Navigation Satellite System (GNSS)‐aided surface reference map by 15.89% relative to the inertial navigation baseline, which supports the effectiveness of the proposed cooperative‐navigation method during the mapping mission. Furthermore, the root mean square error (RMSE) of distance measurements between Leader AUV and Follower AUVs during linear transects decreased by 59.13% and 55.21%, respectively, indicating improved Leader–Follower range consistency achieved by the proposed method. In a Weihai open‐sea campaign with a desired Leader–Follower separation of 100 m on straight‐line segments, the resulting separation RMSE was 6.29 and 4.41 m for the two Followers, respectively. Moreover, in a Danjiangkou surface test with continuous GNSS ground truth (GNSS used only as an external reference, not as filter inputs, except for mission‐start initialization where applicable), the Followers' horizontal RMSE decreased from 15.09 m/13.00 m (INS) to 9.38 m/8.71 m (cooperative).
ABSTRACT As a critical component for robotic citrus harvesting, the mechanical design of the end‐effector determines harvesting efficiency, success rate, and fruit quality. This paper aims to enhance citrus harvesting efficiency by proposing an integrated gripper‐shear harvesting end‐effector and its control method suitable for harvesting operations in Ehime No. 38 Citrus orchards. First, the fruits were collected and their phenotypic characteristics and mechanical properties were measured. On the basis of mechanical design theory and CAE analysis methods, the flexible clamping mechanism and the sliding shear mechanism were developed. Second, a low‐cost clamping force control method combining finite element analysis and linear regression was proposed. Its validity was verified through transient dynamic analysis experiments conducted in Ansys Workbench, involving 15 clamping experiments (10 for initial testing and 5 for validation). Finally, 50 harvesting trials conducted in citrus orchards demonstrated a 96.0% success rate for stalk severing and 94.0% success rate for harvesting undamaged citrus fruits. The proposed harvesting end‐effector is easy to control, simple to maintain, low in manufacturing cost, minimally damaging to citrus, and highly successful in harvesting.
ABSTRACT Odontomas, classified as hamartomas rather than true neoplasms, are the most common type of odontogenic tumors. These lesions are typically asymptomatic, show no gender predilection, and are often detected incidentally through routine radiological examinations. Complete surgical resection is the gold standard for odontoma treatment. Traditional approaches are limited by surgeon‐dependent precision, while digital guide plate‐assisted surgery has fixed procedural constraints. This study reports the first clinical application of a semi‐active oral robotic surgical system for the minimally invasive resection of a compound odontoma embedded in the alveolar bone of the left mandibular body between the mandibular left canine (tooth 33) and first premolar (tooth 34) in a 15‐year‐old female patient. Preoperative planning was completed with high‐resolution CBCT and dedicated robotic navigation software. The odontoma was completely removed without damage to adjacent teeth. The recovery was uneventful, and a 6‐month follow‐up showed excellent bone healing with no residual lesion. The use of oral robots for odontoma extraction enables precise positioning and minimally invasive removal, offering a promising approach for the treatment of these tumors.
ABSTRACT Dual‐arm mobile manipulators can transport and manipulate large objects with simple end‐effectors. Interacting with dynamic environments subject to strict safety and compliance requirements, achieving whole‐body motion planning online while meeting various hard constraints for highly redundant mobile manipulators poses a significant challenge. We tackle this challenge by presenting an efficient whole‐body motion‐planning approach based on model‐based predictive control (MPC). We construct a hierarchical MPC framework in which the first MPC optimizes the end‐effectors' collision‐free motion, thereby guiding the second MPC to optimize whole‐body joint trajectories. In the first stage, we employ a Bézier‐curve representation to parameterize the high‐degree‐of‐freedom (high‐DOF) motion in of two collaborating end‐effectors, notably introducing a novel and efficient method to optimize quaternion trajectories. This facilitates fast long‐horizon motion planning of coupled translational and rotational trajectories while accounting for approximated feasibility constraints. In the second stage, this is the first work to novelly incorporate the representation into a whole‐body MPC for online high‐DOF motion generation with predictive admittance control over a relatively short horizon while satisfying whole‐body hard constraints. Compared with the usage of a discretization approach, our whole‐body MPC ensures accurate model‐state transitions with faster, more stable computation speeds, and consistent motion command generation which enhances tracking performance for our hybrid position/velocity‐controlled robot. Both MPCs perform replanning in each control loop to adapt to a changing environment. Simulations and real‐world experiments validate the proposed hierarchical MPC framework, demonstrating that its two novel MPCs achieve efficient and robust performance in scenarios involving static and dynamic obstacle avoidance, compliant interaction with manipulated objects, and external disturbances.
ABSTRACT The design of a hexapod is complex and requires integration between kinematic models, control systems, and sensing. Existing literature has reviewed these sub‐systems in isolation. Since 2021, there has been no review of the field despite significant advancements in soft soil, lunar traversal, and artificial intelligence. This paper addresses that gap by establishing a novel terrain‐coded locomotion layer scaffold via taxonomy creation. A subset of 58 primary studies were deeply analyzed and synthesized to quantify method choices across hexapod locomotion. Findings show that the DH‐Parameter system and geometric inverse kinematics models are most widely used across all terrain types. Jacobian‐based inverse kinematic models prove to be computationally demanding, yet more accurate. Jacobian approaches also result in multiple end‐effector positions and unnatural poses. Bio‐inspired control systems and proprioceptive methods show promise for smoother gait switching and real‐time adaptability. Approaches related to reinforcement learning, convolutional neural networks and long short‐term memory models were also introduced in recent years. These contribute toward increasing performance, terrain adaptability and path planning. The primary contribution of this paper is a set of data‐driven design patterns linking mathematical models, layered controls, and simulator‐terrain couplings for structured and unstructured environments. These patterns support more defensible deployment‐oriented design choices. Future directions and generalized guidelines based on these design patterns for hexapod locomotion are also highlighted. Thus documenting future‐facing research which shows the hexapod becoming a fully autonomous system capable of harsh terrain traversal (lunar surfaces, ice, soft soil, underwater).
ABSTRACT Agricultural robots are increasingly used to improve orchard productivity and reduce labor dependence, yet collaborative orchard mapping remains difficult because dense canopy degrades GNSS reliability, repetitive row geometry induces structural aliasing, and long‐duration operation over large orchard blocks causes mergeable overlap to emerge only after extended traversal, thereby destabilizing generic registration pipelines. In this work, we formulate orchard map merging as an operation‐time progressive submap association and registration problem for a dual‐robot field setting and propose PROMO‐Orchard, an orchard‐aware semantic map‐merging framework. The framework integrates four tightly coupled components: class‐dependent voxelization with persistent orchard‐prior extraction, Orchard‐AKS for operation‐aware submap scheduling, Sem‐OREOS for semantic‐enhanced overlap retrieval and merge triggering, and Sem‐GICP for class‐weighted semantic refinement with orchard structural regularization. Together, these components turn semantic map merging into an operation‐time, orchard‐aware process rather than a generic post‐hoc registration step. Field experiments in three representative orchard settings, namely I‐Tunnel, U‐Tunnel, and Long‐Duration, show that the proposed method consistently suppresses row aliasing and maintains semantically coherent shared maps under in‐row overlap, headland U‐turning, and delayed‐overlap operation. Compared with Segregator and a semantic‐ablation variant, the proposed framework reduces ICP error by 78–94% while maintaining FPMR above 91% across all scenarios, and achieves clear improvements in semantic fidelity, boundary continuity, and structural consistency. These results support the conclusion that reliable orchard map merging requires not only semantic cues, but their structured integration with orchard‐specific priors, thereby providing a practical basis for collaborative orchard mapping and downstream robotic operations such as navigation, canopy management, harvesting, and logistics. While the present study experimentally validates the dual‐robot case, the proposed progressive pairwise merging formulation provides the system‐level basis for future extension to larger multi‐robot orchard teams.
ABSTRACT This paper presents a comprehensive review of robotics research in search and rescue (SAR) operations conducted in caverns, underground environments, disaster zones, and other areas where Global Navigation Satellite System (GNSS) signals are unavailable. The majority of applications for Simultaneous Localisation and Mapping (SLAM), despite its maturity, are still restricted to structured indoor settings or outdoor environments under normal weather conditions. Standard SLAM frameworks often experience degradation and malfunction or even fail when deployed in increasingly complex and unstructured scenarios. This review identifies three major challenges that robots face in SAR environments: (i) increasingly complex terrain, (ii) changing environments and visibility, and (iii) autonomous exploration requirements, along with corresponding technological evolutions in robot mobility, sensor technologies, and SLAM algorithms. A comprehensive and quantitative evaluation of existing approaches is provided, focusing on SLAM on uneven terrain, multisensor fusion, and active SLAM. Additionally, this paper outlines ongoing challenges for guiding future development toward more robust and reliable deployment‐oriented SLAM solutions for SAR applications. These include: (i) short‐term dynamics and structural changes that undermine data association and loop closure, (ii) observability loss and degeneracy in confined and cluttered spaces, and (iii) multirobot consistency under constrained communication. Two cross‐cutting constraints, which are sensor non‐stationarity and safety‐critical autonomy, are highlighted as key factors that turn deployable SAR SLAM into a system‐level reliability problem. Finally, potential research directions and a practical research roadmap toward robust, real‐time, and evaluable SAR SLAM are outlined.
ABSTRACT Lightweight and high‐strength materials are important in robotics, as structural design impacts efficiency, payload capacity, and energy consumption. Composite materials, with their superior stiffness‐to‐weight ratios and multifunctional properties, offer clear advantages over conventional metals and polymers. This review critically examines the use of composites in robotics, with a focus on their structural, active, and sensory functions. A systematic literature review based on an adapted PRISMA framework identified over 100 publications from 1988 to 2026, revealing continuous research growth and rising interest in soft robotics, piezoelectric composites, and carbon‐based structures. In contrast, bioinspired systems have declined due to integration and manufacturing challenges. Despite the progress, major barriers remain, particularly scalability, long‐term reliability, and cost. This work proposes a functional taxonomy of composites in robotics and outlines future directions, including sustainable bio‐based materials, multimaterial additive manufacturing, and 4D‐printed adaptive systems. By integrating materials science and robotics, this review provides a concise roadmap, in composite structures, for developing high‐performance, multifunctional, and sustainable robotic technologies.
This study presents a novel and unified framework for modeling hybrid terrestrial mobile robots with flippers, tracks, and wheels, explicitly addressing chassis pose control. Using a differential kinematic approach, the unified model combines a generic chassis and locomotion mechanisms models, and is specifically tailored for an actively actuated tracked vehicle. The introduction of two motion groups and four distinct controllers enables precise manipulation of roll, pitch, and ground clearance. The proposed models' algorithms, implemented in the Robot Operating System, are simple, efficient, and easily embedded in a robot designed for industrial services. Experiments conducted with the prototype in laboratory, open-field, and industrial environments evaluate the proposed methodology. The results show that the differential models properly coordinate the locomotion mechanisms, allowing the chassis to achieve the desired input velocities.
Loop closing is crucial for correcting drift in ego-localization and mapping. Current approaches face a critical precision-recall trade-off. To ensure precision and accurate loop pose estimation, traditional methods that impose strict geometric verification inevitably suffer from low recall. Moreover, existing semantic methods, while addressing perceptual aliasing, have yet to effectively utilize semantic information to enhance the recall of geometrically valid loop candidates. In response, we propose a novel loop-closing method that integrates geometric and semantic verification to enhance loop recall while strictly maintaining precision under the same geometric verification. To effectively utilize semantic information, we utilize a semantic topological graph to organize semantic details. To measure the similarity between semantic topological graphs, we propose semantic object associations after long intervals. This association leverages geometric constraints, appearance similarity, and coarse-grained object similarity, effectively formulating object associations as a linear matching problem. Finally, we implement an object-level Bundle Adjustment method that accurately computes geometric transformations between matching keyframes, to improve loop recall and trajectory estimation accuracy. Experimental results demonstrate that the proposed object associations, even after long intervals, can handle dense, occluded, and small objects. Moreover, our loop closing significantly improves loop recall rates and trajectory estimation accuracy, while maintaining strict geometric consistency, as validated on the KITTI and KITTI-360 data sets.
To enhance adaptability and obstacle-crossing performance in unstructured environments, this study proposes a large-expansion-ratio deformable mobile platform, referred to as the "Wheel-Claw Climber," which is based on spatial folding and linkage deformation mechanisms. The platform incorporates a radially expandable deformable wheel with a maximum expansion ratio of 2.72, substantially exceeding the existing benchmark of 2.4. The "Wheel-Claw Climber" operates in three distinct configurations-wheel, claw, and intermediate-enabling adaptation to diverse terrains. An obstacle-crossing model is established, and the maximum obstacle-crossing height is determined to be 4.5R, representing a 27.7% improvement over current designs and demonstrating superior obstacle-surmounting capability. Furthermore, a cross-slope traversal strategy is proposed, whereby deformation and expansion of the low-side wheel relative to the high-side wheel increase passability and stability by 31.1% and 21.65%, respectively, on the same slope. Experimental validation confirms that the Wheel-Claw Climber can seamlessly transition between wheel and claw modes. At a wheel rotation speed of 1 rad/s, the platform achieves a maximum obstacle-crossing height of 550 mm, with a 100% success rate for obstacles below 450 mm. Overall, the deformable wheel architecture and experimental findings provide new insights for the design and development of next-generation mobile platforms.
Crawler robots represent a vital subclass of mobile robots, widely deployed in unstructured field environments. On complex, uneven terrain, track slippage (TS) is almost unavoidable. In addition, signal time delay (STD) is common in sensing and actuation processes, further increasing control complexity. As a result, the coupling of TS and STD poses significant challenges to the accuracy and smoothness of path tracking control (PTC) in crawler robots. Recognizing the strengths of pure pursuit (PP), notably its robustness and straightforward structure, we set out to address the above challenges by improving the pure pursuit method. We propose a PTC method that incorporates a look-ahead heading error compensation (LHEC) algorithm and a PP controller, achieving real-time adjustment of the control inputs by calculating the heading deviation between the look-ahead point and the crawler robot and feeding it back to the control loop as a dynamic compensation signal. This method provides a robust solution to the challenges posed by TS and STD without requiring exhaustive systemic modeling, effectively leveraging the inherent ability of these factors to mitigate oscillations under specific conditions, as we found, thereby enhancing both tracking accuracy and smoothness simultaneously. According to the real-world experiment results, our control method has high accuracy, with the maximum absolute displacement error of 0.0762 m across all experiments. The proposed method can reduce the maximum absolute displacement error by at least 41.34% compared to state-of-the-art yaw-rate-compensated methods, including pure pursuit, nonlinear model predictive control, and Stanley control. Moreover, the proposed method also exhibits superior smoothness. The average yaw jerk did not exceed 13.95 rad/s3. Compared with state-of-the-art yaw rate compensation methods based on pure pursuit or Stanley control, the proposed method can reduce the average absolute yaw jerk by at least 19.55%. Furthermore, 15 sets of repeated trials on continuous curve paths in plowed dry land demonstrate that the controller maintains high consistency. By the way, this study clarifies the inherent limitations of look-ahead distance adjustment and yaw rate compensation strategies under the coupled influence of TS and STD, providing new insights for the development of robust field-robotic control.
ABSTRACT As underwater robotics advances, simulation platforms have become essential for enhancing research, development, and operational strategies. These platforms are crucial because they lower vehicle fabrication costs, mitigate risks, and recreate intricate marine environments. This review offers a detailed examination of prominent underwater simulators, emphasizing essential factors such as environmental modeling, robot kinematics and dynamics, control systems and navigation, sensor emulation, communications and their integration with artificial intelligence and machine learning workflows. We have given special focus to hydrodynamic modeling, visual rendering, and the simulation of realistic underwater phenomena like turbidity, wave interactions, and marine habitat dynamics. The review also evaluates each simulator's effectiveness in operator training, technology validation, and planning for multi‐robot missions. By comparing their designs, advantages, limitations, and specific applications, this study aims to assist in choosing suitable simulation tools. It also outlines potential developments to improve simulation accuracy and interoperability within underwater robotics.
Picking robots often encounter significant challenges when navigating unstructured agricultural environments due to obstacles such as dense branches, immature crops, and other obstacles. This paper presents a Sampling Step Guiding Rapidly-exploring Random Tree (SSG-RRT) path planning algorithm for wheeled picking robots. The proposed algorithm addresses key issues, including excessive redundancy in sampling points, low tree expansion efficiency, poor convergence guidance, and abrupt path turns by constructing a Sampling Step Rapidly-exploring Random Tree (SS-RRT) algorithm that combines a greedy biased sampling strategy and adaptive step size, and further integrating the Artificial Potential Field (APF) algorithm to achieve convergence-oriented optimization. Additionally, a reconnection optimization strategy is employed to eliminate unnecessary path nodes that do not account for obstacles, and cubic B-spline curve smoothing is applied to refine the generated path. To further improve local obstacle avoidance, the Dynamic Window Approach (DWA) is integrated with SSG-RRT. The DWA algorithm tracks the globally planned path generated by SSG-RRT while dynamically adjusting the local path based on velocity constraints to avoid obstacles in real time. Compared with the SS-RRT algorithm, simulation results demonstrate that the SSG-RRT algorithm reduces path length by 21.6%, sampling time by 87.5%, and overall planning time by 84.1%. The proposed approach is successfully applied to real-time obstacle avoidance for both static and dynamic obstacles, effectively addressing challenges such as poor convergence, excessive path inflection points, and weak dynamic obstacle avoidance capabilities in complex and dynamic picking environments.
Uncrewed underwater vehicles (UUVs) have transformed oceanographic research through autonomous data gathering. Similarly, lake and other aquatic research can potentially be automated and transformed. However, UUVs would need to be smaller, lighter, less complex, and cheaper than currently available to make them more practical and user-friendly lake research tools. Conventionally powered micro-UUVs are entering the market, but micro-glider UUV (which uniquely provides long-duration mobile monitoring capability) development has largely been the preserve of "hobbyists." Here, we present innovative design guidelines for a micro-glider UUV that features the novel use of a single actuator to achieve control in three dimensions. This minimizes UUV size, weight, and complexity, and optimizes maneuverability to promote suitability for use in lakes. Presented is a comprehensive and dynamic modeling technique that uses three external and three internal principal glider dimensions to explore the complete micro-glider UUV design space. This model can be used to predict transitory and steady-state glider performance and ultimately optimize design parameters. The prototype proof-of-concept testing has demonstrated practical validation of the design and modeling work. This work is significant as it explores an innovative micro-glider UUV design for lake and reservoir applications; perhaps of greater importance, the modeling approach presented can form the base for micro-glider UUV design. This will allow the development of enhanced automated monitoring capabilities across a broad range of aquatic systems and promote understanding of the freshwater and marine worlds.
ABSTRACT Unmanned mining technology is essential for enhancing safety, increasing efficiency, and reducing operational costs. The complex and hazardous nature of mining environments demands advanced positioning systems for autonomous vehicles, with laser simultaneous localization and mapping (SLAM) algorithms playing a critical role. This paper provides a systematic review of the core technical modules within laser SLAM algorithms, analyzing their development trends, strengths, and weaknesses. A comprehensive evaluation of fifteen mainstream SLAM algorithms on the AutoMine open‐pit mining data set reveals significant insights. Experimental results demonstrate that traditional feature‐based algorithms are prone to significant trajectory drift due to feature loss in sparse‐feature mining environments. Notably, the study further identifies a specific “Ramp Drift” mechanism where recursive estimators suffer Z ‐axis instability on monotonic slopes. Comparative analysis suggests that while Light Detection and Ranging (LiDAR)–inertial fusion generally enhances robustness, degeneracy‐aware architectures are the decisive factor for stability. Specifically, the LiDAR‐only odometry GLO achieves the highest stability in relative pose error due to its Weighted Elastic Matching strategy, while the Adaptive‐LIO demonstrates superior global consistency in absolute pose error. This study highlights a current lack of SLAM architectures specifically optimized for the unique challenges of open‐pit mines. Future research should focus on feature extraction enhancement in open scenes, the development of optimized LiDAR–inertial–RTK fusion architectures, and the integration of artificial intelligence to improve adaptability in dynamic and degraded scenarios.
ABSTRACT This review article examines jet‐propulsion mechanisms in underwater soft robotic systems, focusing exclusively on physically fabricated and experimentally validated robots. Covering research published from 2013 to 2025, this study classifies and evaluates jet‐propulsion robots based on their actuation mechanisms. This review outlines the fundamental working principles, discusses the key advantages and limitations, and assesses the practicality of these mechanisms for underwater applications. Additionally, a list of robots employing each actuation method is presented, illustrating the diversity of approaches within the field. By systematically comparing these studies, this review identifies critical performance trade‐offs, potential challenges, and opportunities for innovation in jet‐propulsion‐based underwater robotics. Ultimately, this work serves as a valuable resource for researchers and engineers, facilitating advancements in the design and application of bioinspired underwater jetting robots.
This paper discusses lap time optimization, focusing on a single lap without considering opponents in autonomous racing. The paper presents a control and optimization architecture composed of a model-based low level controller and a higher level iterative learning algorithm with the goal of obtaining the fastest qualifying lap in autonomous racing competitions. First principles models are extremely expensive to calibrate near the handling limit, to solve this issue our algorithm learns the position varying acceleration limits of the vehicle over multiple laps. The proposed algorithm brings together the robustness and generalization capability of model-based approaches with the performance of data-driven methods. To validate the approach and its computational efficiency, we implement the solution on a high performance small scale vehicle and test it against a human driver on a racing track with speed up to 50 km/h and lateral accelerations of 1.2 g. The proposed approach beats a national level champion in terms of qualifying lap for small scale vehicles, on the considered test track.
