This paper proposes a LiDAR-inertial odometry (LIO) based on the dynamic voxel merging and smoothing method, DV-LIO. In this approach, a local map management mechanism based on feature distribution is introduced to unify the features of similar adjacent voxels through dynamic merging and segmentation, thereby improving the perceptual consistency of environmental features. Moreover, a novel noise detector that performs noise detection and incremental filtering by evaluating the consistency of voxel features is designed to further reduce local map noise and improve mapping accuracy while ensuring real-time algorithm performance. Meanwhile, to ensure the computational efficiency of the LIO system, a point cache is set for each voxel, which allows the voxel to be updated incrementally and intermittently. The proposed method is extensively evaluated on datasets gathered over various environments, including campus, park, and unstructured gardens.

This paper presents an efficient trajectory planning method for a 4-DOF robotic arm designed for pick-and-place manipulation tasks. The method addresses several challenges, where traditional optimization approaches struggle with high dimensionality, and data-driven methods are costly to collect enough data. The proposed approach leverages Bézier curves for computationally efficient, smooth trajectory generation, minimizing abrupt changes in motion. When continuous solutions for the end-effector angle are unavailable, joint angles are interpolated using Bézier or Hermite interpolation. Additionally, we use custom metrics to evaluate deviation between the interpolated trajectory and the original trajectory, as well as the overall smoothness of the path. When a continuous solution exists, the trajectory is treated as a Gaussian process, where a prior factor is generated using the centerline. This prior is then combined with a smoothness factor to optimize the trajectory, ensuring it remains as smooth as possible within the feasible solution space through stochastic gradient descent. The method is evaluated through simulations in Nvidia Isaac Sim; results highlight the method’s suitability, and future work will explore enhancements in prior trajectory integration and smoothing techniques.

Treating inertial measurement unit (IMU) measurements as inputs to a motion model and then preintegrating these measurements have almost become a de facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple asynchronous IMUs. In this paper, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. We methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method’s hyperparameters have been tuned on a training set. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. In simulation, our approach exceeds the performance of an imu-as-input baseline during highly aggressive motion. On the Newer College Dataset, we demonstrate state-of-the art results. These results show that continuous-time techniques and the treatment of the IMU as a measurement of the state are promising areas of further research. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icp.

Continuum robot-based surgical systems are becoming an effective tool for minimally invasive surgery. A flexible, dexterous, and compact robot structure is suitable for carrying out complex surgical operations. In this paper, we propose performance metrics for dexterity based on data density. Data density at a point in the workspace is higher if the number of reachable points is higher, with a unique configuration lying in a small square box around a point. The computation of these metrics is performed with forward kinematics using the Monte Carlo method and, hence, is computationally efficient. The data density at a particular point is a measure of dexterity at that point. In contrast, the dexterity distribution property index is a measure of how well dexterity is distributed across the workspace according to desired criteria. We compare the dexterity distribution property index across the workspace with the dexterity index based on the dexterous solid angle and manipulability-based approach. A comparative study reveals that the proposed method is simple and straightforward because it uses only the position of the reachable point as the input parameter. The method can quantify and compare the performance of different geometric designs of hyper-redundant and multisegment continuum robots based on dexterity.

Visual Simultaneous Localization and Mapping (vSLAM) is essentially limited by the static world assumption, which makes its application in dynamic environments challenging. This paper proposes a robust vSLAM system, RFN-SLAM, which is based on ORB-SLAM3 and does not require preset dynamic labels and weighted features to process dynamic scenes. In the feature extraction stage, an enhanced efficient binary image BAD descriptor is used to improve the accuracy of static feature point matching. Through the improved RT-DETR target detection network and FAST-SAM instance segmentation network, RFN-SLAM obtains semantic information and uses a novel dynamic box detection algorithm to identify and eliminate the feature points of dynamic objects. When optimizing the pose, the static feature points are weighted according to the dynamic information, which significantly reduces the mismatch and improves the accuracy of positioning. Meanwhile, 3D rendering of the neural radiation field is used to remove dynamic objects and render them. Experiments were conducted on the TUM RGB-D dataset, Bonn dataset, and self-collected dataset. The results show that in terms of positioning accuracy, RFN-SLAM significantly outperforms ORB-SLAM3 in dynamic environments. It also achieves more accurate positioning than other advanced dynamic SLAM methods and successfully realizes accurate 3D reconstruction of static scenes. In addition, on the premise of ensuring accuracy, the real-time performance of RFN-SLAM is effectively guaranteed.

In response to the auxiliary requirements for the treatment and prevention of lumbar diseases, based on the biomechanical characteristics of the human waist, a novel unpowered rigid-flexible coupling waist exoskeleton with multiple degrees of freedom and its human-exoskeleton parallel wearable equivalent research prototype are proposed, further focusing on the encompassing kinematic compatibility and dynamic load-bearing effectiveness of the biomimetic coordination, an in-depth analysis is performed on the multi-body dynamic dimensional synthesis and its methodological research. Initially, based on the rigid-flexible coupling characteristics and experimental biomechanical data of the lumbar region in the sagittal plane, an accurate multi-body system dynamics model of the research prototype, which incorporates the rigid-flexible coupling characteristics, is systematically constructed. Subsequently, to effectively quantify the biomimetic coordination of the exoskeleton, a novel comprehensive optimization index, termed biomimetic load-bearing comfort, is proposed. Finally, by utilizing this index, the exoskeleton is optimized in dimension by employing a thorough combination of multi-dimensional spatial search algorithm and compression factor particle swarm algorithm. The simulation results validate the correctness and effectiveness of both the dynamic dimensional synthesis and its methodology. Furthermore, the study also reveals that the optimized exoskeleton’s passive working mode showcases favorable biomimetic coordination. These results are crucial for progressing the research on the biomimetic load-bearing capacities of other exoskeletons.

Pipeline inspection robots play a crucial role in maintaining the integrity of pipeline systems across various industries. In this paper, a novel pipeline inspection robot is designed based on a four degrees-of-freedom (DOF) generalized parallel mechanism (GPM). First, a four DOF mechanism is introduced using numerical and graph synthesis. The design employs numerical and graph synthesis methods to achieve an ideal symmetric configuration, enhancing the robot’s adaptability and mobility. The coupling mid-platform, inspired by parallelogram mechanisms, enables synchronized contraction motion, allowing the robot to adjust to different pipe diameters. Then, the constraints of the pipeline inspection robot in the elbow are analyzed based on task requirements. Through kinematic and performance analyses using screw theory, the mechanism’s feasibility in practical applications is confirmed. Theoretical analysis, simulations, and experiments demonstrate the robot’s ability to achieve active steering in T-branches and elbows. Experimental validation in straight and bent pipes shows that the robot meets the expected speed targets and can successfully navigate complex pipeline environments. This research highlights the potential of GPMs in advancing the capabilities of pipeline inspection robots for real-world applications.

Continuous-time batch state estimation using Gaussian processes is an efficient approach to estimate the trajectories of robots over time. In the past, relatively simple physics-motivated priors have been considered for such approaches, using assumptions such as constant velocity or acceleration. This paper presents an approach to incorporating exogenous control inputs, such as velocity or acceleration commands, into the continuous Gaussian process state estimation framework. It is shown that this approach generalizes across different domains in robotics, making it applicable to both the estimation of continuous-time trajectories for mobile robots and the estimation of quasi-static continuum-robot shapes. Results show that incorporating control inputs leads to more informed priors, potentially requiring less measurements and estimation nodes to obtain accurate estimates. This makes the approach particularly useful in situations in which limited sensing is available. For example, in a mobile robot localization experiment with sparse landmark distance measurements and frequent odometry control inputs, our approach provides accurate trajectory estimates with root-mean-square errors around 3-4 cm and 4-5 degrees, even with time intervals up to five seconds between discrete estimation nodes, which significantly reduces computation time.

This paper introduces the Arachne System, a scalable, cost-effective mobile microrobot swarm platform designed for educational and research applications. It details the design, functionalities, and potential of the Arachne Bots, emphasizing their accessibility to users with minimal robotics expertise. By providing a comprehensive overview of the system’s hardware, sensory capabilities, and control algorithms, the paper demonstrates the platform’s capacity to democratize and reduce entry barriers in mobile robotic swarms research, fostering innovation and educational opportunities in the field. Extensive experimental validation of the system showcases its broad range of capabilities and effectiveness in real-world implementation.

The paper presents an enhanced method for unknown parameter estimation and nonlinear controller adaptation that combines the concept of unfalsification with the genetic algorithm (GA). This approach is based on the measured data and employs a bank of nonlinear controllers designed to dynamically adjust to the system’s evolving conditions. The controllers in the bank can be switched to meet the system’s requirements. This method is applied to an autonomous underwater vehicle (AUV) with uncertain parameters. Using the unfalsification method, these uncertain parameters are estimated, and a suitable controller is selected from the bank to guide the AUV along a desired trajectory. Additionally, an artificial intelligence technique, such as the GA, is employed to update the controller bank, resulting in versatile and optimised candidates. The simulation results obtained in the MATLAB/Simulink environment show that in the environment considered in this paper, the Adaptive Unfalsification algorithm in conjunction with GA estimates the unknown parameter values better than the sole GA-optimised values. Also, the convergence of the actual trajectory of the AUV using the Adaptive Unfalsification algorithm in conjunction with GA is faster and better than the sole GA-optimised algorithm. Furthermore, a survey of experimental results from established literature is included to evaluate the practical implementation of the proposed design, which concludes that within a reasonable time, the Adaptive Unfalsification algorithm in conjunction with GA can be implemented in commercially available processors.

This work proposes an optimization approach for the time-consuming parts of Light Detection and Ranging (LiDAR) data processing and IMU-LiDAR data fusion in the LiDAR-inertial odometry (LIO) method. Two key novelties enable faster and more accurate navigation in complex, noisy environments. Firstly, to improve map update and point cloud registration efficiency, we employ a sparse voxel maps with a new update function to construct a local map around the mobile robot and utilize an improved Generalized Iterative Closest Point algorithm based on sparse voxels to achieve LiDAR point clouds association, thereby boosting both map updating and computational speed. Secondly, to enhance real-time accuracy, this paper analyzes the residuals and covariances of both IMU and LiDAR data in a tightly coupled manner, and achieves system state estimation by fusing sensor information through Gauss-Newton method, effectively mitigating localization deviations by appropriately weighting the LiDAR covariances. The performance of our method is evaluated against advanced LIO algorithms using eight open datasets and five self-collected campus datasets. Results show a 24.7–60.1% reduction in average processing time per point cloud frame, along with improved robustness and higher precision motion trajectory estimation in most cluttered and complex indoor and outdoor environments.

This paper presents a climbing robot (CR) designed for the purpose of pipeline maintenance, with capability to avoid the risks inherent in manual operations. In the design process, a three degree of freedom (DOF) parallel mechanism coupled with a remote center of motion (RCM) mechanism linkage mechanism were designed to serve as the CR’s climbing mechanism, which met the specific demands for climbing movements. The modified Kutzbach–Grübler formula and the screw theory were applied to calculate the DOFs of the CR. Then, the inverse and forward position analysis for the CR was derived. Furthermore, velocity and acceleration analysis of parallel mechanism were conducted and derived the Jacobian matrix, through which the singularity of parallel mechanism was analyzed. In order to evaluate kinematic performance of parallel mechanism, the motion/force transmission index (LTI) of workspace was calculated, which directed the followed dimensional optimization process. According to the optimization result, a prototype was constructed and a series of motion experiments were carried out to validate its climbing capability.

Kinematically redundant parallel mechanisms (PMs) have attracted extensive attention from researchers due to their advantages in avoiding singular configurations and expanding the reachable workspace. However, kinematic redundancy introduces multiple inverse kinematics solutions, leading to uncertainty in the mechanism’s motion state. Therefore, this article proposes a method to optimize the inverse kinematics solutions based on motion/force transmission performance. By dividing the kinematically redundant PM into hierarchical levels and decomposing the redundancy, the transmission wrench screw systems of general redundant limbs and closed-loop redundant limbs are obtained. Then, input, output, and local transmission indices are calculated, respectively, to evaluate the motion/force transmission performance of such mechanisms. To address the problem of multiple inverse kinematics solutions, the local optimal transmission index is employed as a criterion to select the optimal motion/force transmission solution corresponding to a specific pose of the moving platform. By comparing performance atlas before and after optimization, it is demonstrated that the optimized inverse kinematics solutions enlarge the reachable workspace and significantly improve the motion/force transmission performance of the mechanism.

A modeling method of multi-objective optimization design for parallel mechanisms (PMs) is proposed, whose implementation is illustrated with 2RPU-RPS mechanism as an example. The orientation of biased output axis on moving platform is depicted by spherical attitude angles, and its kinematic model is deduced through vector method. With screw theory as mathematic tool, a comprehensive evaluation method of kinematic performance for PM is established. On this basis, the expensive constrained multi-objective optimization model of dimensional parameters for the discussed mechanism is constructed. The NSDE-II algorithm, formed by replacing the genetic algorithm operators in non-dominated sorting genetic algorithm II (NSGA-II) with DE operators, is utilized to solve this multi-objective optimization problem, thus obtaining multiple Pareto optimal solutions with engineering application significance, which proves the feasibility and effectiveness of the proposed modeling method and algorithm. Moreover, the normalization coverage space and the minimum adjacent vector angle are proposed to evaluate the computational performance of NSDE-II. Finally, the potential engineering application value for the optimized 2RPU-RPS PM is presented.

Screw theory serves as an influential mathematical tool, significantly contributing to mechanical engineering, with particular relevance to mechanism science and robotics. The instantaneous screw and the finite displacement screw have been used to analyse the degree of freedom and perform kinematic analysis of linkage mechanisms with only lower pairs. However, they are not suitable for higher pair mechanisms, which can achieve complex motions with a more concise structure by reasonably designing contact contours, and they possess advantages in some particular areas. Therefore, to improve the adaptability of screw theory, this paper aims to analyse higher kinematic pair (HKP) mechanisms and proposes a method to extend instantaneous screw and finite displacement screw theory. This method can not only analyse the instantaneous degree of freedom of HKP mechanisms but also determine the relationships between the motion variables of HKP mechanisms. Furthermore, this method is applied to calculate the degree of freedom and the relationships between the motion angles in both planar and spatial cam mechanisms, thereby demonstrating its efficiency and advantages.

Upper extremity rehabilitation robots have become crucial in stroke rehabilitation due to their high durability, repeatability, and task-specific capabilities. A significant challenge in assessing the comfort performance of these robots is accurately calculating the human-robot interaction forces. In this study, a four-degree-of-freedom (4-DOF) upper extremity rehabilitation robot mechanism, kinematically compatible with the human upper limb, is proposed. Based on this mechanism, an algorithm for estimating human-robot interaction forces is developed using Newton-Euler dynamics. A prototype of the proposed robot is constructed, and a series of comparative experiments are carried out to validate the feasibility of the proposed force estimation approach. The results indicate that the proposed method reliably predicts interaction forces with minimal deviation from experimental data, demonstrating its potential for application in upper limb rehabilitation robots. This work provides a foundation for future studies focused on comfort evaluation and optimization of rehabilitation robots, with significant practical implications for improving patient rehabilitation outcomes.

This paper deals with the optimization of a new redundant spherical parallel manipulator (New SPM). This manipulator consists of two spherical five-bar mechanisms connected by the end-effector, providing three degrees of freedom, and has an unlimited self-rotation capability. Three optimization procedures based on the genetic algorithm method were carried out to improve the dexterity of the New SPM. The first and the second optimizations were applied to a symmetric New SPM structure, while the third was applied to an asymmetric New SPM structure. In both cases, the optimization was performed using an objective function defined by the quadratic sum of link angles. In addition, certain criteria and constraints were implemented. The obtained results demonstrate significant improvements in the dexterity of the New SPM and its capability of an unlimited self-rotate in an extended workspace. A comparison of the self-rotation performances between the classical 3-RRR SPM (R for revolute joint) and the New SPM is also presented.

This paper presents a novel control framework for achieving collision-free trajectory tracking in tractor-trailer mobile robots (TTMRs) within both static and dynamic environments. This study addresses the challenges posed by nonholonomic constraints and kinematic coupling inherent in TTMR systems. An inverse kinematic control strategy, augmented with pure integral controllers, is proposed to ensure precise trajectory tracking. The control framework is further enhanced using three obstacle avoidance approaches: two customized artificial potential field (APF) approaches and a novel path planning mode (PPM) for continuous-time trajectory adjustment. APF methods, applied for the first time to TTMR trajectory tracking, incorporate gravitational and repulsive forces to guide the robot away from obstacles, whereas PPM dynamically generates a semi-circular trajectory when the robot approaches an obstacle. Case studies validate the effectiveness of the proposed strategy in accurate trajectory tracking and safe obstacle avoidance. Comparative analyses highlight the superior performance of PPM in managing complex environments.

In response to the prevailing trend of an aging society and the increasing requirements of rehabilitation, this paper presents an approach involving brain-machine interaction (BMI) for a single-degree-of-freedom (1-DOF) sit-to-stand transfer robot. Based on a 1-DOF rehabilitation robot, three experiment paradigms involving motor imagery (MI), action observation of motor imagery (AO-MI) and motor execution are designed using both electroencephalography (EEG) and electromyography (EMG). To enhance motion intention recognition accuracy, a Gumbel-ResNet-KANs decoding model is established. The Gumbel-ResNet-KANs model integrates the Gumbel-Softmax method with the ResNet-KANs network module and demonstrates strong decoding capability, as demonstrated by comparative tests in this paper. To validate the effect of robotic assistance, EEG and EMG coherence are analyzed to assess the impact of robotic assistance on rehabilitation from a neuromuscular perspective in both assisted and unassisted conditions. We assessed the effect of robotics on rehabilitation from an emotional perspective by analyzing the difference between the differential entropy of the right and left brain. The proposed study also reveals that the movement-related cortical potentials in AO-MI are beneficial for promoting the performance of BMI in sit-to-stand training, which provides a possible approach for the development of new types of robots for lower limb rehabilitation.

The path navigation of robot in an entirely known space is presented by various researchers in the recent times. The navigational complexity arises when a robot moves in a completely unknown and complex environment from one defined start to a designated desired location. As the success of the nature-inspired algorithms in the unclear navigational problem is better, therefore, an improved butterfly optimization algorithm (IBOA) to determine the optimal feasible path for a humanoid robot navigating through a platform cluttered with both known and unfamiliar barriers is presented in this study. The BOA is inspired by the food-gathering habits of butterflies, where the sense of smell is the vital parameter in the global optimal search. However, the performance of this technique in the complex environment is poor, as a result, the chances of being trapped in local minima are more. Hence, the BOA is improved by using a nonlinear weight reduction strategy in updating the position of the butterflies in every iteration. The simulation is carried out in the Webots platform by considering variable-legged robot, NAO, in an unfamiliar environment. The outcomes derived from the simulation and real assessments demonstrate the potential of the proposed technique and compare with other existing algorithms, which highlights the potential and efficacy of the proposed IBOA algorithm.