To meet the high-precision positioning requirements for hybrid machining units, this article presents a geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit (HKMU) with five axis. A minimal kinematic error modeling of the serial–parallel HKMU is established with screw-based method after elimination of redundant errors. A set of composite error indices is formulated to describe the terminal accuracy distribution characteristics in a quantitative manner. A modified projection method is proposed to determine the actual compensable and noncompensable source errors of the HKMU by identifying such transformable source errors. Based on this, the error compensation and comparison analysis are carried out on the exemplary HKMU to numerically verify the effectiveness of the proposed modified projection method. The geometric error evaluations reveal that the parallel module has a larger impacts on the terminal accuracy of the platform of the HKMU than the serial module. The error compensation results manifest that the modified projection method can find additional compensable source errors and significantly reduce the average and maximum values of geometric errors of the HKMU. Hence, the proposed methodology can be applied to improve the accuracy of kinematic calibration of the compensable source errors and can reduce the difficulty and workload of tolerance design for noncompensable source errors of such serial–parallel hybrid mechanism.

The article considers a method for obtaining a rational layout of the design of a multi-robotic system for aliquoting biomaterial, consisting of two robots with different architectures, based on the analysis of safety zones. The first robot has a serial structure and ensures the continuous operation of the second robot, performing auxiliary work related to the movement of biological samples. The second robot, which is a parallel robot, directly performs the workflow of extracting and dosing liquid into aliquots. An algorithm for determining the safety zones for each of the robots is presented, based on which the optimal mutual arrangement of the two robots is obtained. Three-dimensional models of safety zones were created, on the basis of which the digital design of the mounting frame for the two robots was performed using the method of topological optimization of material distribution in the structure. This made it possible to obtain a rational design of the mounting frame, which does not intersect with the safety zones of the robots. The surface of the robotic system mounting frame, obtained as a result of topological optimization, is transformed into a metal structure suitable for manufacturing. The strength characteristics of two variations of the mounting frame are compared: the first one, obtained through topological optimization, and its transformed analog made from a standard profile.

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

Lower limb-assisted exoskeletons can provide payloads and support, but the hip joints of current lower limb-assisted exoskeletons have problems such as single mechanisms, few degrees of freedom, and limitations in the types of gaits that can be acted upon. To address these problems, a novel double-cam hip joint assist mechanism is proposed for walking, running, and carrying gait situations. The double cam and two rectangular compression springs with different stiffness are used to satisfy the differences in exoskeleton assistance requirements in multiple gaits. First, biomechanical simulation of walking, running, and carrying movements is carried out with OpenSim to obtain the hip joint angle and torque data, and then the structural design of the assist mechanism is carried out. Hip joint angle planning and contour solving are carried out for the contour lines of the cams, so that the cam contour lines are assist according to different hip joint angles, and the stiffness of compression springs is determined by D’Alembert’s principle, and the assist torques are analyzed. Meanwhile, a human–machine coupling model was established for theoretical analysis. Finally, the muscle power change curves were exported using OpenSim, and the assistance was verified by comparison.

The integration of extra-robotic limbs or fingers to enhance and extend motor capabilities, particularly for grasping and manipulation, remains a major challenge. In contrast to the natural human hand, which achieves highly dexterous and adaptive grasping, the performance of current extra-robotic limbs or fingers is still markedly limited. Human hands can detect the onset of slip through tactile feedback originating from tactile receptors during the grasping process, enabling precise and automatic regulation of grip force. This grip force is scaled by the coefficient of friction between the contacting surface and the fingers. The frictional information is perceived by humans depending upon the slip happening between the finger and the object. This ability to perceive friction allows humans to apply just the right amount of force needed to maintain a secure grip, adjusting based on the weight of the object and the friction of the contact surface. Enhancing this capability in extra-robotic limbs or fingers used by humans is challenging. To address this challenge, this paper introduces a novel approach to communicate frictional information to users through encoded vibrotactile cues. These cues are conveyed on the onset of incipient slip, thus allowing the users to perceive the friction and ultimately use this information to increase the force to avoid dropping the object. In a 2-alternative forced-choice protocol, participants gripped and lifted a glass under three different frictional conditions, applying a normal force of 3.5 N. After reaching this force, the glass was gradually released to induce slip. During this slipping phase, vibrations scaled according to the static coefficient of friction were presented to users, reflecting the frictional conditions. The results suggested an accuracy of $94.53\pm 3.05$ ($\text{mean}\pm \text{SD}$) in perceiving frictional information upon lifting objects with varying friction. The results indicate the effectiveness of using vibrotactile feedback for sensory feedback, allowing users of extra-robotic limbs or fingers to perceive frictional information. This enables them to assess surface properties and adjust grip force according to the frictional conditions, enhancing their ability to grasp and manipulate objects more effectively.

Descriptions of various subsets of $\mathbb{SO}(3)$ are encountered frequently in robotics, for example, in the context of specifying the orientation workspaces of manipulators. Often, the Cartesian concept of a cuboid is extended into the domain of Euler angles, notwithstanding the fact that the physical implications of this practice are not documented. Motivated by this lacuna in the existing literature, this article focuses on studying sets of rotations described by such cuboids by mapping them to the space of Rodrigues parameters, where a physically meaningful measure of distance from the origin is available and the spherical geometry is intrinsically pertinent. It is established that the planar faces of the said cuboid transform into hyperboloids of one sheet and hence, the cuboid itself maps into a solid of complicated non-convex shape. To quantify the extents of these solids, the largest spheres contained within them are computed analytically. It is expected that this study would help in the process of design and path planning of spatial robots, especially those of parallel architecture, due to a better and quantitative understanding of their orientation workspaces.

Connecting individual robots to form an inter-reconfigurable system with a flexible base size enhances the ability to access and cover areas for cleaning and maintenance tasks. Given that increased configuration complexity expands the search space dimension, an optimal routing solution ensuring efficiency is essential. In this paper, we present an inter-reconfigurable multi-robot system capable of adjusting the bases of its two units, along with an optimal path planning approach for confined spaces based on a modified informed rapidly-exploring random tree algorithm by a greedy set (RIRRT*). We validate the navigation of the proposed inter-reconfigurable platform using RIRRT* for four informed dimensional search spaces as a case study in both simulated and real-world environments. The proposed path planning method for the inter-reconfigurable system outperformed conventional strategies, achieving significant reduction in both execution time and energy utilization.

Workspace analysis is a crucial step in designing any robotic system and ensuring its safe operation. This article analyzes the workspace of a six degree-of-freedom (6-DOF) hybrid robot, which includes two separate modules with parallel architectures placed above each other. The upper module is a 4-DOF Delta-type parallel mechanism, and the lower module is a 2-DOF rotary mechanism with a circular rail. With this design, the hybrid robot represents a relative manipulation system, and workspace analysis is performed in the relative motion of the modules. This approach differs from other similar studies that combine the workspaces determined for each module independently, and we propose a method and derive results more suitable for practical use. To solve the workspace analysis problem, the paper develops a discretization-based approach, which considers all mechanical constraints. These constraints include joint constraints of each module and link interference between the modules. To analyze this interference, we apply the Gilbert–Johnson–Keerthi algorithm and represent the links as convex polytopes. Multiple numerical examples illustrate the developed techniques and show the translation and orientation workspaces of the robot for various relative configurations of its modules. Computer-aided-design simulations validate the proposed theoretical algorithms. The results demonstrate that the link interference between the modules, often ignored in other works, limits the workspace and should be considered for the proper workspace evaluation and design of similar hybrid robots.

Soft robots show an advantage when conducting tasks in complex environments due to their enormous flexibility and adaptability. However, soft robots suffer interactions and nonlinear deformation when interacting with soft and fluid materials. The reason behind is the free boundary interactions, which refers to undetermined contact between soft materials, specifically containing nonlinear deformation in air and nonlinear interactions in fluid for soft robot simulation. Therefore, we propose a new approach using material point method (MPM), which can solve the free boundary interactions problem, to simulate soft robots under such environments. The proposed approach can autonomously predict the flexible and versatile behaviors of soft robots. Our approach entails incorporating automatic differentiation into the algorithm of MPM to simplify the computation and implement an efficient implicit time integration algorithm. We perform two groups of experiments with an ordinary pneumatic soft finger in different free boundary interactions. The results indicate that it is possible to simulate soft robots with nonlinear interactions and deformation, and such environmental effects on soft robots can be restored.

A finite-time adaptive composite integral sliding mode control strategy based on a fast finite-time observer is proposed for trajectory tracking of the Stewart parallel robot, considering unmodeled uncertainties and external disturbances. First, a global finite-time converging sliding mode surface composed of intermediate variables and integral terms is established to eliminate steady-state tracking errors. Next, a fast finite-time extended state observer is designed to compensate for uncertainties and external disturbances, improving the robustness of the control system. Finally, based on this, a finite-time sliding mode control rate is designed. The gain value is adjusted through an adaptive reaching law to reduce sliding mode chattering, and global finite-time convergence of the system is theoretically proven using Lyapunov theory. Experimental verification shows that the proposed control strategy has stronger robustness to uncertainties and external disturbances, faster error convergence, less chattering, and higher stability accuracy.

The variable stiffness actuator (VSA) excels at tasks that are challenging for traditional rigid mechanisms to perform. A novel variable stiffness tensegrity-based compliant actuator is proposed, following an analysis of the cons and pros of existing VSAs. The proposed actuator leverages a tensegrity structure to eliminate direct contact between rigid elements, thereby reducing the internal mechanical friction. This leads to low damping and compliant behavior. Additionally, it enables a wide range of stiffness adjustments and decouples rotational stiffness from the rotation angle by utilizing different variants of the mechanically adjustable compliance and controllable equilibrium position actuator (MACCEPA). The stiffness analysis of the single-joint actuator is presented and experimentally validated. This design is then extended to multi-joint mechanism applications, including serial mechanism configuration, wire-driven mechanism configuration, and direct-drive mechanism configuration. An evaluation of the structural characteristics of these three configurations is provided, offering different options for implementing VSAs. The conducted works could provide fresh insights into the field of VSA.

The two main sources of difficulty for a group of mobile robots employing sensors to find a source are robot collisions and wireless ambient noise, such as light, sound, and other sounds. This paper introduces a novel approach to multi-robot system cooperation and collision avoidance: the new modified source-seeking control with noise cancelation technology. The robot team works together on an incline of a light source field; the team’s mobility is dependent upon following the upward gradient’s direction and forming a particular movement pattern. The proposed program also takes into account each robot’s size, speed limit, obstacles, and noise. The noise cancelation technique has been used to avoid the delay and false decisions to find the target point of the source. When the noise is canceled, all control inputs to the algorithm are accurate, and the feedback decision will be true. In this study, we use the MATLAB simulation tools to test the velocity, position, time delay, and performance of each robot in the used group of robots. The simulation and practical results of the robots in searching for a light source showed very satisfactory performance compared with the results in the literature.

In this article, the performance analysis and multiobjective structure optimization of 4RRR parallel mechanism are carried out. Firstly, the 4RRR pure rotation parallel mechanism and its design route are introduced. Secondly, the Jacobian matrices in 2DoF pure rotation and 3DoF pure rotation modes are derived using the motion equations of the mechanism. Next, the singularity analysis, kinematic dexterity analysis, dynamic dexterity analysis, and stiffness analysis of the mechanism are carried out, respectively, and it is proved that there is no singularity in the mechanism in its workspace. Since the dexterity performance expression is a nonlinear piecewise function, the kinematic local comprehensive dexterity index and the dynamic local comprehensive dexterity index are proposed as the objects of analysis. Furthermore, the kinematic global comprehensive dexterity index, the dynamic global comprehensive dexterity index, and the global comprehensive stiffness index are proposed to carry out the multiobjective structural optimization. Finally, NSGA3 was used to complete the optimization, and the comprehensive optimal solution of the structure size was obtained.

Detecting cracks in underwater dams is crucial for ensuring the quality and safety of the dam. However, underwater dam cracks are easily obscured by aquatic plants. Traditional single-view visual inspection methods cannot effectively extract the feature information of the occluded cracks, while multi-view crack images can extract the occluded target features through feature fusion. At the same time, underwater turbulence leads to nonuniform diffusion of suspended sediments, resulting in nonuniform flooding of image feature noise from multiple viewpoints affecting the fusion effect. To address these issues, this paper proposes a multi-view fusion network (MVFD-Net) for crack detection in occluded underwater dams. First, we propose a feature reconstruction interaction encoder (FRI-Encoder), which interacts the multi-scale local features extracted by the convolutional neural network with the global features extracted by the transformer encoder and performs the feature reconstruction at the end of the encoder to enhance the feature extraction capability and at the same time in order to suppress the interference of the nonuniform scattering noise. Subsequently, a multi-scale gated adaptive fusion module is introduced between the encoder and the decoder for feature gated fusion, which further complements and recovers the noise flooding detail information. Additionally, this paper designs a multi-view feature fusion module to fuse multi-view image features to restore the occluded crack features and achieve the detection of occluded cracks. Through extensive experimental evaluations, the MVFD-Net algorithm achieves excellent performance when compared with current mainstream algorithms.

Visual exploration is a task in which a camera-equipped robot seeks to efficiently visit all navigable areas of an environment within the shortest possible time. Most existing visual exploration methods rely on a static camera fixed to the robot’s body to control its own movements. However, coupling the orientation of camera with robot’s body limits the extra degrees of freedom to obtain more visual information. In this work, we adjust the camera orientation during robot motion by using a novel camera view planning (CVP) policy to improve the exploration efficiency. Specifically, we reformulate the CVP problem as a reinforcement learning problem. However, two new challenges need to be addressed: 1) determining how to learn an effective CVP policy in complex indoor environments and 2) figuring out how to synchronize it with the robot motion. To solve the above issues, we create a reward function considering factors such as exploration area, observed semantic objects, and the motion conflicts between the camera and the robot’s body. Moreover, to better coordinate the policies of the camera and the robot’s body, the CVP policy takes the body actions and the egocentric 2D spatial maps with exploration, occupancy, and trajectory information into account to make motion decisions. Experimental results show that after using the proposed CVP policy, the exploration area is expanded by 21.72% and 25.6% on average in the small-scale indoor scene with few structured obstacles and large-scale indoor scene with cluttered obstacles, respectively.

In this paper, a novel special structure derived from the double-slider mechanism is presented and apply it to the design of a parallel mechanism, endowing the designed parallel mechanism with reconfigurable characteristics. First, a comprehensive analysis of the motion modes of the double-slider mechanism is carried out. By ingeniously varying the slider structure of the double-slider mechanism, a special structure capable of three distinct motion modes is obtained. This special structure is then integrated into the 3UPU parallel mechanism. As a result, the redesigned 3UPU parallel mechanism exhibits reconfigurability and can seamlessly switch among the three motion modes. Subsequently, the inverse kinematics, workspace, and singular pose of the parallel mechanism in these three modes are meticulously analyzed. Moreover, the Jacobian matrix is utilized to evaluate its flexibility and load-bearing performance. The analysis reveals that in different motion modes, all performance indicators of the mechanism are remarkable, indicating a promising application prospect. Finally, a prototype is fabricated using 3D printing technology to further validate the effectiveness of the proposed special structure. Additionally, its versatility is further explored and analyzed in-depth.

The multi-robot path planning problem is an NP-hard problem. The coati optimization algorithm (COA) is a novel metaheuristic algorithm and has been successfully applied in many fields. To solve multi-robot path planning optimization problems, we embed two differential evolution (DE) strategies into COA, a self-adaptive differential evolution-based coati optimization algorithm (SDECOA) is proposed. Among these strategies, the proposed algorithm adaptively selects more suitable strategies for different problems, effectively balancing global and local search capabilities. To validate the algorithm’s effectiveness, we tested it on CEC2020 benchmark functions and 48 CEC2020 real-world constrained optimization problems. In the latter’s experiments, the algorithm proposed in this paper achieved the best overall results compared to the top five algorithms that won in the CEC2020 competition. Finally, we applied SDECOA to optimization multi-robot online path planning problem. Facing extreme environments with multiple static and dynamic obstacles of varying sizes, the SDECOA algorithm consistently outperformed some classical and state-of-the-art algorithms. Compared to DE and COA, the proposed algorithm achieved an average improvement of 46% and 50%, respectively. Through extensive experimental testing, it was confirmed that our proposed algorithm is highly competitive. The source code of the algorithm is accessible at: https://ww2.mathworks.cn/matlabcentral/fileexchange/164876-HDECOA.

Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.

Underactuated Cable-Driven Parallel Robots (UACDPRs) typically rely on relative internal sensors to estimate the end-effector (EE) state. Therefore, at startup, the reference values of the quantities measured by these sensors are unknown, and so is the initial pose of the EE. The problem of determining the reference values of the internal sensors is called initial-pose self-calibration. The latter is often formulated as an overdetermined system of nonlinear equations and solved using nonlinear weighted least-squares methods, minimizing the error between modeled and measured variables, and its effectiveness is highly influenced by the choice of measurement configurations, as well as the motion planning and control strategy used to reach them. This work presents two practical data acquisition methods for initial-pose self-calibration of UACDPRs, aiming to reduce the overall time required by the procedure and enhance process automation. The first method is slower but richer in data, as it relies on equilibrium poses and, therefore, can leverage cable-tension data, whereas the second method is faster and is based on geometric constraints only. The performance of the methods is evaluated in terms of acquisition time, number of measurements, and calibration accuracy on a 4-cable UACDPR prototype. The results highlight the merits and shortcomings of both methods, namely, trade-offs between the velocity of data collection and the precision of pose estimation.

This research proposes an adaptive human-robot interaction (HRI) that combines voice recognition, emotional context detection, decision-making, and self-learning. The aim is to overcome challenges in dynamic and noisy environments while achieving real-time and scalable performance. The architecture is based on a three-stage HRI system: voice input acquisition, feature extraction, and adaptive decision-making. For voice recognition, modern pre-processing techniques and mel-frequency cepstral coefficients are used to robustly implement the commands. Emotional context detection is governed by neural network classification on pitch, energy, and jitter features. Decision-making uses reinforcement learning where actions are taken and then the user is prompted to provide feedback that serves as a basis for re-evaluation. Iterative self-learning mechanisms are included, thereby increasing the adaptability as stored patterns and policies are updated dynamically. The experimental results show substantial improvements in recognition accuracy along with task success rates and emotional detection. The proposed system achieved 95% accuracy and a task success rate of 96%, even against challenging noise conditions. It is apparent that emotional detection achieves a high F1-score of 92%. Real-world validation showed the system’s ability to dynamically adapt, thus mitigating 15% latency through self-learning. The proposed system has potential applications in assistive robotics, interactive learning systems, and smart environments, addressing scalability and adaptability for real-world deployment. Novel contributions to adaptive HRI arise from the integration of voice recognition, emotional context detection, and self-learning mechanisms. The findings act as a bridge between the theoretical advancements and the practical utility of further system improvements in human-robot collaboration.