Onboard localization for multi-robot systems stands as a critical area of research with wide-ranging applications. This paper introduces an innovative framework for multi-robot localization, uniquely characterized by its onboard capability, thereby negating the dependency on external infrastructure. Our approach harnesses the inherent capabilities of each robot, enabling them to localize and synchronize their movements independently. The integration of cooperative localization algorithms with formation control mechanisms empowers a group of robots to sustain a predefined formation while following a linear trajectory. The efficacy of our framework is substantiated through comprehensive simulations and real-world experimental validations. We rigorously assess the system’s resilience to localization inaccuracies and external disturbances, demonstrating its adaptability and consistency in maintaining formation under diverse conditions. Furthermore, we explore the scalability of our approach, highlighting its potential to manage varying numbers of robots and its applicability in tasks such as collaborative transportation.

This article introduces a dome-type soft tactile sensor that can autonomously adjust its stiffness to evaluate surface contact characteristics, including the elastic modulus, contact force, and the presence of abnormal hardness within soft materials, using a strain gauge as a single sensing element. The strain sensor element is placed at the tip of the dome to measure the deformations during contact that reflect the properties of the contacted object. Using machine learning techniques, the sensor system can accurately predict these characteristics in various materials with an error rate of less than approximately 8%. A hybrid approach that combines experimental and simulation data enables the sensor to be trained effectively, generating sufficient data for accurate predictions without extensive experiments. The high accuracy results of the machine learning models demonstrate that the sensor system can precisely calculate the elastic modulus and depth of the defect. The adaptability and precision of the proposed sensor make it ideal for applications in medical diagnostics and other fields requiring careful interaction with soft materials. Furthermore, its innovative approach can be referenced for exploiting the properties of soft materials to achieve task-specific morphology without redesigning soft sensors or soft robots.

The snake robot can be used to monitor and maintain underwater structures and environments. The motion of a snake robot is achieved by lateral undulation which is called the gait pattern of the snake robot. The parameters of a gait pattern need to be adjusted for compensating environmental uncertainties. In this work, 3D motion dynamics of a snake robot for the underwater environment is proposed with vertical motion using the buoyancy variation technique and horizontal motion using lateral undulation. “The neutral buoyant snake robot motion in hypothetical plane and added mass effect is negligible”, these previous assumptions are removed in this work. Two different control algorithms are designed for horizontal and vertical motions. The existing super twisting sliding mode control (STSMC) is used for the horizontal serpentine motion of the snake robot. The control law is designed on a reduced-ordered dynamic system based on virtual holonomic constraints. The vertical motion is achieved by controlling the mass variation using a pump. The water pumps are controlled using the event-based controller or Proportional Derivative (PD) controller. The results of the proposed control technique are verified with various external environmental disturbances and uncertainties to check the robustness of the control approach for various path following cases. Moreover, the results of STSMC scheme are compared with SMC scheme to check the effectiveness of STSMC. The practical implementation of the work is also performed using Simscape Multibody environment where the designed control algorithm is deployed on the virtual snake robot.

Grasp detection is a significant research direction in the field of robotics. Traditional analysis methods typically require prior knowledge of the object parameters, limiting grasp detection to structured environments and resulting in suboptimal performance. In recent years, the generative convolutional neural network (GCNN) has gained increasing attention, but they suffer from issues such as insufficient feature extraction capabilities and redundant noise. Therefore, we proposed an improved method for the GCNN, aimed at enabling fast and accurate grasp detection. First, a two-dimensional (2D) Gaussian kernel was introduced to re-encode grasp quality to address the issue of false positives in grasp rectangular metrics, emphasizing high-quality grasp poses near the central point. Additionally, to address the insufficient feature extraction capabilities of the shallow network, a receptive field module was added at the neck to enhance the network’s ability to extract distinctive features. Furthermore, the rich feature information in the decoding phase often contains redundant noise. To address this, we introduced a global-local feature fusion module to suppress noise and enhance features, enabling the model to focus more on target information. Finally, relevant evaluation experiments were conducted on public grasping datasets, including Cornell, Jacquard, and GraspNet-1 Billion, as well as in real-world robotic grasping scenarios. All results showed that the proposed method performs excellently in both prediction accuracy and inference speed and is practically feasible for robotic grasping.

Cable-guiding mechanisms (CGMs) and the stiffness characteristics directly influence the dynamic features of the cable-driven upper limb rehabilitation robot (PCUR), which will affect PCUR’s performance. This paper introduces a novel CGM design. Given the precision and movement stability considerations of the mechanism, an analytical model is developed. Using this model, we analyze the error of the CGM and derive velocity and acceleration mappings from the moving platform to the cables. Continuity of cable trajectory and tension is rigorously demonstrated. Subsequently, a mathematical model for PCUR stiffness is formulated. Utilizing MATLAB/Simscape Multibody, simulation models for the CGM and stiffness characteristics are constructed. The feasibility of the proposed CGM design is validated through simulation and experimentation, while the influence of stiffness characteristics on PCUR motion stability is comprehensively analyzed.

The automation of assembly operations with industrial robots is pivotal in modern manufacturing, particularly for multispecies, low-volume, and customized production. Traditional programing methods are time-consuming and lack adaptability to complex, variable environments. Reinforcement learning-based assembly tasks have shown success in simulation environments, but face challenges like the simulation-to-reality gap and safety concerns when transferred to real-world applications. This article addresses these challenges by proposing a low-cost, image-segmentation-driven deep reinforcement learning strategy tailored for insertion tasks, such as the assembly of peg-in-hole components in satellite manufacturing, which involve extensive contact interactions. Our approach integrates visual and forces feedback into a prior dueling deep Q-network for insertion skill learning, enabling precise alignment of components. To bridge the simulation-to-reality gap, we transform the raw image input space into a canonical space based on image segmentation. Specifically, we employ a segmentation model based on U-net, pretrained in simulation and fine-tuned with real-world data, significantly reducing the need for labor-intensive real image segment labels. To handle the frequent contact inherent in peg-in-hole tasks, we integrated safety protections and impedance control into the training process, providing active compliance and reducing the risk of assembly failures. Our approach was evaluated in both simulated and real robotic environments, demonstrating robust performance in handling camera position errors and varying ambient light intensities and different lighting colors. Finally, the algorithm was validated in a real satellite assembly scenario, achieving a success rate of 15 out of 20 tests.

Locomotion control of inchworm robots presents significant challenges due to their highly nonlinear dynamics and complex interactions with the environment. Traditional control methods often struggle with achieving precise tracking and adaptive performance in dynamic conditions. To address these limitations, this article proposes a novel data-driven compound control system that integrates fractional proportional-integral derivative (FPID) control with Koopman operator theory. Unlike conventional approaches, which rely on direct nonlinear control or simplified linear approximations, our method leverages data-driven modeling to transform the nonlinear dynamics into a linear representation, making control design more systematic and scalable. A deep neural network is trained to identify the Koopman operator, enabling an FPID controller to operate within this transformed space for improved tracking accuracy and robustness. The proposed framework is validated using NVIDIA Isaac SIM simulation software, demonstrating superior locomotion efficiency and tracking performance compared to existing control strategies. This study advances the control of bio-inspired robots by bridging fractional-order control with data-driven Koopman-based modeling, addressing the fundamental challenge of achieving high-precision locomotion in complex environments.

With the increasing manufacturing of electric vehicles, car battery recycling is crucial for environmental sustainability. The disassembly of car batteries includes critical health hazards for the operator, due to potential chemical reactions or physical injuries. These reasons make robots particularly interesting for automatic disassembly. This paper proposes a systematic approach to automation and human–robot cooperation in car battery disassembly tasks with a case study on screw removal. A novel parameter is proposed to evaluate whether a human operator or a robot is more appropriate for each specific task, considering both performance and associated risks. The proposed metrics are validated with an experimental example, in which the performance of a robot and a human on a screw-removal task is compared numerically using statistical methods. The advantages and disadvantages of both options are examined through the application and show how the new performance criterion effectively provides insights into the distribution of tasks between humans and robots.

Bilateral teleoperation has witnessed significant development since the mid-20th century, a23ressing challenges related to human presence in environments with constraints or a lack of skilled professionals. This article presents the kinematic and self-collision analyses of the quasi-spherical parallel manipulator, a three-legged parallel robot used as a haptic master device. The device is designed for remote center of motion-constrained operation in the telesurgical field. Inverse and forward kinematics are thoroughly analyzed to study working modes, singular configurations, and implement a haptic control architecture. The research explores the operative and reachable workspaces of the possible working modes, comparing them to find the most suitable one. Results highlight how the addition of the self-collision phenomenon impacts the working mode choice, drastically reducing most of the modes’ operative workspaces. An anti-collision control algorithm is finally introduced to maintain the architecture within its reachable workspace.

Precise and efficient grasping detection is vital for robotic arms to execute stable grasping tasks in industrial and household applications. However, existing methods fail to consider refining different scale features and detecting critical regions, resulting in coarse grasping rectangles. To address these issues, we propose a real-time coarse and fine granularity residual attention (CFRA) grasping detection network. First, to enable the network to detect different sizes of objects, we extract and fuse the coarse and fine granularity features. Then, we refine these fused features by introducing a feature refinement module, which enables the network to distinguish between object and background features effectively. Finally, we introduce a residual attention module that handles different shapes of objects adaptively, achieving refined grasping detection. We complete training and testing on both Cornell and Jacquard datasets, achieving detection accuracy of 98.7% and 94.2%, respectively. Moreover, the grasping success rate on the real-world UR3e robot achieves 98%. These results demonstrate the effectiveness and superiority of CFRA.

Human–machine compatibility and collaborative control for stroke patients utilizing lower limb rehabilitation robots have attracted considerable research attention. As a highly human–machine-coupled system, ensuring adequate compliance and safety is fundamental to efficient and comfortable rehabilitation. Therefore, this paper first quantifies human–machine contact interactions, proposes a human–machine coupling dynamics modeling method, and identifies the robot’s dynamic inertia parameters and human lower limb parameters. Second, a dual closed-loop controller for the rehabilitation robot is designed. Based on the bottom position control, an adaptive admittance control algorithm is proposed that employs the root-mean-square propagation (RMSprop) algorithm to tune the adaptive gain. In rehabilitation training, the controller can adaptively adjust the admittance parameters according to the human–machine interaction force to achieve responsiveness to the dynamic changes of the human–machine system. The experimental results of the control system show that the human–machine cooperative control performance is significantly improved, the maximum joint angle error is reduced by more than 40.9%, and the maximum human–machine interaction force is reduced by more than 19.4%.

6D pose estimation can perceive an object’s position and orientation in 3D space, playing a critical role in robotic grasping. However, traditional sparse keypoint-based methods generally rely on a limited number of feature points, restricting their performance under occlusion and viewpoint variations. To address this issue, we propose a novel Neighborhood-aware Graph Aggregation Network (NGANet) for precise pose estimation, which combines fully convolutional networks and graph convolutional networks (GCNs) to establish dense correspondences between 2D–3D and 3D–3D spaces. The $K$-nearest neighbor algorithm is integrated to build neighborhood relationships within isolated point clouds, followed by GCNs to aggregate local geometric features. When combined with mesh data, both surface details and topological shapes can be modeled. A positional encoding attention mechanism is introduced to adaptively fuse these multimodal features into a unified, spatially coherent representation about pose-specific features. Extensive experiments indicate that our proposed NGANet achieves a higher estimation accuracy on LINEMOD and Occlusion-LINEMOD datasets. In addition, its effectiveness is also validated under real-world scenarios.

Rehabilitation treatment is often labor-intensive and time-consuming, but it also lacks quantitative and objective assessment. With regard to the matter of balance rehabilitation machines, the continuous advancement of parallel robot technology provides new solutions for balance rehabilitation. However, these robots have inherent limitations, including a confined workspace, excessive height, a complex structure, and unstable movement due to singularity in workspace. Therefore, this study presents a new 3-2PUS double-triangular construction mechanism with six degrees of freedom for use in balance rehabilitation therapy. First, the forward and inverse kinematic models are established, and then the Newton–Raphson method is employed to resolve the forward kinematics. Subsequently, the velocity model is analyzed and its singular configuration is determined. Finally, the workspace of the 3-2PUS parallel mechanism is delineated, and the findings indicate that its structure is compact and that the workspace is free of singularities. This ensures that the rehabilitative devices will remain stable throughout the rehabilitation process, thus preventing any additional injuries that might otherwise result from unstable movement. To validate the study of the full parallel mechanism, a series of simulations is conducted using computational analysis software. Based on the analysis results, a prototype of a balance rehabilitation parallel manipulator is presented.

This paper presents the design and dynamic analysis of a reconfigurable four-wheeled mobile robot, with front wheels capable of transforming from a conventional circular wheel into a five-spoke wheel-legged (wheg) configuration. The transformation is achieved through a reconfiguration mechanism integrating a slider-crank chain with a rack and pinion system. A comprehensive dynamic analysis of the mechanism is conducted to evaluate the torque requirements for actuation and to support the selection of a suitable off-the-shelf motor. The required actuation torque is primarily influenced by the normal contact (reaction) force between the wheel and the ground or terrain, which varies depending on surface or terrain conditions. This contact force is computed using system dynamics, and its variations are further analyzed through the robot’s dynamic response. Numerical simulations, supported by real-world field tests, validate the effectiveness of the proposed design in moderately uneven environments.

Robot hands are essential components of robots; however, the hand of more complex spatial mechanisms with coupling chains is rarely proposed. This paper proposes a hybrid hand with three underactuated finger plane limbs connected by a flexible closed-loop chain. The degree of freedom (DOF) of the hybrid hand is equal to the number of motors before grasping the object. When the contact force appears between the fingertips and the object, the flexible linkages deform, allowing the hybrid hand to maintain adaptability during contact. As the three fingers make contact with the object, the hybrid hand forms a closed-loop chain with the object, ensuring that the overall DOF remains consistent with the number of motors. Firstly, the hybrid hand’s structural characteristics and DOF are analyzed. Secondly, the kinematics of the hybrid hand are derived, and the relationships among the spring deformation, the kinematics of the fingertip and the input of the hybrid hand are obtained according to the geometric constraints. Thirdly, based on the kinematic results and the principle of virtual work method, the coupling dynamics formula of the hybrid hand is established, and the relationship between the dynamic driving force, dynamic constrained force, spring force and the force acting on the object is solved. Finally, the simulation model of the hybrid hand is constructed in MATLAB to validate the theoretical solution, and the merits of the hybrid hand were confirmed by prototype experiments. This paper aims to support a theoretical foundation for the intelligent control of novel hybrid hands.

Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.

Outdoor mobile robots must navigate uneven terrains with obstacles that sometimes cannot be avoided; therefore, strategies have been developed for robots to overcome them. In most cases, these strategies have been modeled considering movement over horizontal surfaces and with the robot positioned directly in front of the obstacle, but this idealization does not occur in most real cases. Therefore, this article describes a strategy for obstacle overcoming, useful when the robot faces obstacles on inclined terrains and at an oblique angle relative to the robot’s trajectory, considering rollover stability during the process, based on the reaction force criterion. This strategy can be used by mobile robots with wheels and an articulated arm whose end effector can contact the ground, and it consists of a sequence of standard movements that include the use of the arm, whose variable location was defined through a system developed using fuzzy logic. The designed strategy was validated through simulations and then implemented on the Lázaro robot, verifying its effectiveness through experimental tests. With it, the robot can overcome obstacles such as steps, ramps, and ditches from any position; additionally, it increased the ability to overcome obstacles with a height close to twice the radius of the robot’s wheels.

The path planning and obstacle-crossing motion planning of cable trench inspection robots are essential for achieving automated inspection. To improve path planning efficiency and obstacle navigation in complex environments, an enhanced global path planning algorithm based on the A* algorithm has been developed, combined with an improved Dynamic Window Approach (DWA) for local path planning. For unavoidable obstacles, a specific obstacle-crossing motion planning strategy has been formulated. The enhanced A* algorithm improves efficiency and safety through adaptive neighborhood expansion and the elimination of redundant path points. The improved DWA algorithm enables real-time dynamic obstacle avoidance in local path planning. The simulation results on a $20 \times 20$ grid map indicate that the improved A* algorithm reduces the number of nodes by 58.4% and shortens the path length by 6.1% compared to the traditional A* algorithm, demonstrating significant advantages over other conventional path planning algorithms. In the simulation experiments integrating global and local path planning, the enhanced A* algorithm combined with the improved DWA algorithm reduces the path length by 3.2% on the $20 \times 20$ grid map compared to the integration with the traditional DWA algorithm. On the $30 \times 30$ grid maps with different obstacle configurations, the path lengths are reduced by 3.5% and 3.6%, respectively. In the obstacle-crossing experiments, the robot successfully overcame obstacles of 10 cm and 20 cm in height. The proposed path planning algorithm and obstacle-crossing motion planning strategy hold substantial application potential in complex environments, offering reliable technical support for cable trench inspection robots.

The control of shipborne stabilisation platforms is challenging due to the effects of platform dynamic characteristics and unpredictable wave disturbances in operational environments. This paper proposes an integrated control strategy that combines dynamic feedforward and fuzzy gain control. Based on the derived dynamic model of the shipborne stabilisation platform, a dynamic feedforward controller is designed to mitigate the effects of platform dynamics on motion accuracy. In the fuzzy gain control design, scaling modules are proposed to enhance the fuzzy controller’s adaptability to varying operating conditions and unpredictable wave disturbances. The motion of the stabilisation platform is simulated by taking the motion of the lower platform calculated based on the wave fluctuations in marine environments as the input. The prototype experiment is conducted by using a large-scale parallel mechanism to simulate the wave environments. Simulation and experimental results indicate that the proposed control strategy achieves real-time disturbance compensation without precise mathematical modelling or pre-training, and demonstrates good adaptability.

At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.