Efficient local trajectory optimization of the coordinated manipulator is a bottleneck task in the narrow feeding scenario. To optimize the trajectory locally and generate collision-free trajectories with local support performance, the analytical reinforcement method is proposed. Firstly, multiple coordinated machines operating in the narrow space are transformed into decentralized dynamic constraints for the target manipulator. Combined with the circle envelope method in the dynamic constraint, the collision-free gradient optimization function determines the support region of the local optimal trajectory. Based on the forward kinematics and inverse kinematics method, the collision-prone pose of the target manipulator outside the support region is analytically optimized. And chi-square distribution further ensures the smooth interpolation of the variable-period trajectory outside the fixed-period support region. For the emergency collision avoidance of the coordinated manipulator in the flexible stamping line, the analytical reinforcement method is successfully verified by generating the collision-free and smooth trajectory. It provides an optimization direction for rapidly improving the work efficiency of multi-machine coordination in the narrow feeding scenario.

Parallel manipulators (PMs) are adopted in different fields due to their superior characteristics compared to serial manipulators. PMs with flexible links are likely more energy efficient and have high dynamic performance since they are lighter than those with rigid links. On the other hand, due to their lightweight design, the flexibility can lead to undesired deformation and vibration, decreasing the tracking trajectory and transient errors. This work proposes a two-loop active vibration control strategy, using strain gauges and piezoelectric lead zirconate (PZT) actuators, to compensate for the undesired effect of the flexibility. A pose control loop exploits the sliding mode control using data collected from images acquired by an oCam-5CRO-U camera, while the active vibration control loop uses strain gauge sensors and PZT actuators. Strain gauges are responsible for measuring the deformation of each link, and after being treated by digital filters, these signals are applied to the PZT actuator. Combining both loops allows the manipulator to be guided over the desired trajectory with positive vibration attenuation. The results reveal that the presence of the PZT on both sides of the flexible links increases the links’ rigidity, yielding overshoot and vibration reduction during the manipulator’s motion. In addition, the maximum peak is significantly attenuated, and the overall oscillations are also positively reduced when using the two-loop active control strategy. The root mean square error quantifies this attenuation, showing an average reduction of 30% in the corresponding step input directions. Therefore, the proposal improves the system performance by enhancing the tracking trajectory with lower vibrations.

The study presents a novel cable-driven serial robot based on flexible joints and tensegrity structures, which features a rapid response capability in complex dynamic environments. This makes it particularly suitable for human–robot interaction scenarios. Compared to traditional rigid serial robots, the design’s compliance demonstrates significant advantages in addressing complex demands. The study delves into kinematic and dynamic modeling methods and verifies their effectiveness through simulations. The kinematic model transforms the local coordinate system to the global one using general kinematic equations. First, the static and dynamic model of the robot is derived based on the torque balance equation, and then the dynamic model of the robot is constructed. By simplifying the robot model, the relationship between tension values from driving cables and the robot’s workspace is analyzed under the constraints of tensegrity structures and flexible joints. Additionally, trajectory simulations validate the kinematic and dynamic models. The kinetic energy variation curves based on the trajectories confirm the accuracy of the theoretical analysis. This method demonstrates broad applicability and can be applied to other serial robots with flexible structures, offering effective solutions for use in complex dynamic environments.

Designing autonomous robotic systems for monitoring tasks in critical security scenarios requires more rigorous verification criteria. The losses associated with unsuccessful practical experiments are immeasurable, ranging from the simple loss of high-value-added equipment to those related to loss of life. This reality justifies the need to adopt an extensive framework of tools for realistic, efficient, and responsive computer simulation. This article proposes a novel integration architecture and combines open-source tools to promote the successful implementation of autonomous robotic systems in monitoring tasks. The proposed solution relies on consolidated tools like Robot Operating System (ROS), Gazebo Simulator, and ArduPilot FCU (Flight Control Unit). It includes full support for implementing XITL techniques (such as Model, Software, and Hardware) – in the Loop. Experimental results demonstrate the proposal’s effectiveness for a new model of autonomous surface vehicles (ASVs) in a realistic environment, dedicated to environmental monitoring in challenging natural conditions, commonly found in a stretch of the Madeira River – Brazil, specifically at Santo Antônio hydroelectric plant.

Simultaneous localization and mapping technology is the basis for multi-robot systems to complete navigation, path planning, and autonomous exploration in complex, dynamic, and Global Positioning System (GPS)-denied environments. This paper reviews the current status and progress of multi-robot simultaneous localization and mapping (SLAM) technology based on LiDAR. First, this paper studies the basic principles of LiDAR SLAM. It analyzes the system model construction of LiDAR SLAM, including the mobile robot coordinate system model, kinematic model, sensor model, map presentation, LiDAR SLAM framework, and classic algorithms. Then, this paper discusses the basic framework of collaborative SLAM, analyzes the key issues such as data association, loop closure detection, and global graph optimization in collaborative SLAM, and conducts a detailed literature review on the solutions to key problems in sub-fields of multi-robot SLAM such as frontier detection, task allocation, map fusion, and compares the advantages and disadvantages of various algorithms. Finally, this paper outlines the challenges and future research directions of multi-robot LiDAR SLAM.

Multiple mobile manipulators (MMs) show superiority in the tasks requiring mobility and dexterity compared with a single robot, especially when manipulating/transporting bulky objects. However, closed-chain of the system, redundancy of each MM, and obstacles in the environment bring challenges to the motion planning problem. In this paper, we propose a novel semi-coupled hierarchical framework (SCHF), which decomposes the problem into two semi-coupled sub-problems. To be specific, the centralized layer plans the object’s motion first, and then the decentralized layer independently explores the redundancy of each robot in real-time. A notable feature is that the lower bound of the redundancy constraint metric is ensured, besides the closed-chain and obstacle-avoidance constraints in the centralized layer, which ensures the object’s motion can be executed by each robot in the decentralized layer. Simulated results show that the success rate and time cost of SCHF outperform the fully centralized planner and fully decoupled hierarchical planner significantly. In addition, cluttered real-world experiments also show the feasibility of the SCHF in the transportation tasks. A video clip in various scenarios can be found at https://youtu.be/Y8ZrnspIuBg.

Navigation is an important skill required for an autonomous robot, as information about the location of the robot is necessary for making decisions about upcoming events. The objective of the localization technique is “to know about the location of the collected data.” In previous works, several deep learning methods were used to detect localization, but none of them gives sufficient accuracy. To address this issue, an Enhanced Capsule Generation Adversarial Network and optimized Dual Interactive Wasserstein Generative Adversarial Network for landmark detection and localization of autonomous robots in outdoor environments (ECGAN-DIWGAN-RSO-LAR) is proposed in this manuscript. Here, the outdoor robot localization dataset is taken from the Virtual KITTI dataset. It contains two phases, which are landmark detection and localization. The landmark detection phase is determined using Enhanced Capsule Generation Adversarial Network for detecting the landmark of the captured image. Then the robot localization phase is determined using Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) for determining the robot location coordinates as well as compass orientation from identified landmarks. After that, the weight parameters of the DIWGAN are optimized by Rat Swarm Optimization (RSO) algorithm. The proposed ECGAN-DIWGAN-RSO-LAR is implemented in Python. The efficiency of the proposed ECGAN-DIWGAN-RSO-LAR technique shows higher accuracy of 22.67%, 12.45 %, and 8.89% compared to the existing methods.

Pouch-type actuators have recently garnered significant interest and are increasingly utilized in diverse fields, including soft wearable robotics and prosthetics. This is largely due to their lightweight, high output force, and low cost. However, the inherent hysteresis behavior markedly affects the stability and force control of pouch-type driven systems. This study proposes a modified generalized Prandtl–Ishlinskii (MGPI) model, which includes generalized play operators, the tangent envelope function, and one-sided dead-zone operators, to describe the asymmetric and non-convex hysteresis characteristics of pouch-type actuators. Compared to a classical Prandtl–Ishlinskii (PI) model incorporating one-sided dead-zone functions, the MGPI model exhibits smaller relative errors at six different air pressures, demonstrating its capability to accurately describe asymmetric and non-convex hysteresis curves. Subsequently, the MGPI hysteresis model is integrated with displacement sensing technology to establish a load compensation control system for maintaining human posture. Four healthy subjects are recruited to conduct a 1 kg load compensation test, achieving efficiencies of 85.84%, 84.92%, 83.63%, and 68.86%, respectively.

Safety is an essential requirement as well as a major bottleneck for legged robots in the real world. Particularly for learning-based methods, their trial-and-error nature and unexplainable policy have raised widespread concerns. Existing methods usually treat this challenge as a trade-off between safety assurance and task performance. One reason for this drawback stems from the inaccurate inference for the robot’s safety. In this paper, we re-examine the segmentation of the robot’s state space in terms of safety. According to the current state and the prediction of the state transition trajectory, the states of legged robots are classified into safe, recoverable, unsafe, and failure, and a safety verification method is introduced to online infer the robot’s safety. Then, task, recovery, and fall protection policies are trained to ensure the robot’s safety in different states, forming a safety supervision framework independently from the learning algorithm. To validate the proposed method and framework, experiment results are conducted both in the simulation and on the real-world robot, indicating improvements in terms of safety and efficiency.

The functionality and aesthetic of 3D-printed components can be compromised if visible defects appear on their external surfaces. To overcome this issue, CNC machines were traditionally adopted for milling machining. More recently, industrial robots have been demonstrated to be a valid alternative. This study presents a robotic workstation developed for contouring machining 3D thermoplastic components printed using the material extrusion technology. The workstation adopts a collaborative robot with a novel, custom-designed, and low-cost end-effector made of a powered contouring tool integrated with three load cells for measuring the cutting forces along three perpendicular directions. The tool path planning is defined by a proposed and validated procedure. By a vision algorithm and a touch-stop operation, the 3D CAD model-based tool path is adapted to the current position and orientation of the workpiece. The experimental activity for determining the optimal set of contouring machining parameters (rotational speed, cut depth, and feed rate) and for measuring cutting forces confirms the feasibility of adopting the cobot-based solution for this application and suggests potential improvements for future works.

The remote center of motion (RCM) mechanism is one of the key components of minimally invasive surgical robots. Nevertheless, the most widely used parallelogram-based RCM mechanism tends to have a large footprint, thereby increasing the risk of collisions between the robotic arms during surgical procedures. To solve this problem, this study proposes a compact RCM mechanism based on the coupling of three rotational motions realized by nonlinear transmission. Compared to the parallelogram-based RCM mechanism, the proposed design offers a smaller footprint, thereby reducing the risk of collisions between the robotic arms. To address the possible errors caused by the elasticity of the transmission belts, an error model is established for the transmission structure that includes both circular and non-circular pulleys. A prototype is developed to verify the feasibility of the proposed mechanism, whose footprint is further compared with that of the parallelogram-based RCM mechanism. The results indicate that our mechanism satisfies the constraints of minimally invasive surgery, provides sufficient stiffness, and exhibits a more compact design. The current study provides a new direction for the miniaturization design of robotic arms in minimally invasive surgical robots.

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. This article explores four popular and representative localization methods for object localization in luggage trolley collection: radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. A qualitative evaluation framework is constructed to assess performance, encompassing Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Furthermore, a series of quantitative experiments concerning Localization Accuracy and Success Rate have been conducted on a real-world robotic autonomous luggage trolley collection system. The performance of various localization methods is further analyzed based on experimental results, indicating that the Keypoints method is optimally suited for indoor environments to facilitate luggage trolley collection. Significantly, these experiment results provide a valuable reference point, extending the application of indoor localization methods across diverse scenarios. A website about this work is available at https://sites.google.com/view/localization-evaluation/.

A method is proposed for identifying robot gravity and friction torques based on joint currents. The minimum gravity term parameters are obtained using the Modified Denavit–Hartenberg (MDH) parameters, and the dynamic equations are linearized. The robot’s friction torque is identified using the Stribeck friction model. Additionally, a zero-force drag algorithm is designed to address the issue of excessive start-up torque during dragging. A sinusoidal compensation algorithm is proposed to perform periodic friction compensation for each stationary joint, utilizing the identified maximum static friction torque. Experimental results show that when the robot operates at a uniform low speed, the theoretical current calculated based on the identified gravity and friction fits the actual current well, with a maximum root mean square error within 50 mA, confirming the accuracy of the identification results. The start-up torque compensation algorithm reduces the robot’s start-up torque by an average of $ 60.58\mathrm{\%}$, improving the compliance of the dragging process and demonstrating the effectiveness of the compensation algorithm.

This paper presents a novel robust control method for a hip-assist exoskeleton robot’s joint module, addressing dynamic performance under variable loads. The proposed approach integrates traditional PID control with robust, model-based strategies, utilizing the system’s dynamic model and a Lyapunov-based robust controller to handle uncertainties. This method not only enhances traditional PID control but also offers practical advantages in implementation. Theoretical analysis confirms the system’s uniform boundedness and ultimate boundedness. A Matlab prototype was developed for simulation, demonstrating the control scheme’s feasibility and effectiveness. Numerical simulations show that the proposed fractional-order hybrid PD (FHPD) controller significantly reduces tracking error by 58.70% compared to the traditional PID controller, 55.41% compared to the MPD controller, and 32.32% compared to ADRC, highlighting its superior tracking performance and stability.

Sit-to-stand (STS) motion is an essential daily activity. However, this motion becomes increasingly difficult for older adults as their muscle strength declines with age. To assist individuals in standing up while maximizing their muscle strength based on the assist-as-needed (AAN) strategy, assistive devices must detect early STS intent, specifically before the buttocks leave the chair, to ensure timely assistance. This study proposes a novel method for detecting STS intent by applying external mechanical stimuli to the toes and analyzing the resulting changes in heel and toe-reaction forces. Moreover, a structured detection framework was developed by utilizing predefined thresholds for the change rate and magnitude of the heel and toe-reaction forces to detect STS intent. Offline tests for threshold setting of STS-intent detection were established in the offline tests: change rate and magnitude of the reaction forces on the heel and toes. The thresholds for each criterion were determined using the Pareto optimization method. Using the determined thresholds, these criteria were then applied in online tests to evaluate the performance of the proposed intent detection method. The results demonstrated that mechanical stimuli improved the performance of STS-intent detection, providing accurate and stable detection. This method can be applied to STS-assistive devices to effectively implement AAN functionality for standing assistance.

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

A wrist-hand exoskeleton designed to assist individuals with wrist and hand limitations is presented in this paper. The novel design is developed based on specific selection criteria, addressing all the Degrees of Freedom (DOF). In the conceptual design phase, design concepts are created and assessed before being screened and scored to determine which concept is the most promising. Performance and possible restrictions are assessed using kinematic and dynamic analysis. Using polylactic acid material, the exoskeleton is prototyped to ensure structural integrity and fit. Manual control, master-slave control, and electroencephalography (EEG) dataset-based control are among the control strategies that have been investigated. Direct manipulation is possible with manual control, nevertheless, master-slave control uses sensors to map user motions. Brain signals for hand opening and closing are interpreted by EEG dataset-based control, which manages the hand open-close of the exoskeleton. This study introduces a novel wrist-hand exoskeleton that improves usefulness, modularity, and mobility. While the numerous control techniques give versatility based on user requirements, the 3D printing process assures personalization and flexibility in design.

Cartographer is an algorithm that was open sourced by Google in 2016 and adapted to multiple sensors. To address issues of the original algorithm, such as the negative impact of outlier point cloud on the scan matching, and low accuracy of position fusion. This paper preprocesses the sensor data and presents HT-Carto, an improved hybrid point-cloud filtering system, and a tightly coupled LiDAR/IMU framework based on Cartographer’s front-end. The inertial measurement unit (IMU) provides initial values for the point cloud, and the IMU pre-integration combines the scan-matched pose to construct the factors, which are added as constraints to the factor graph. The result is used to update the current pose and work as odometer residuals at the back-end. The optimization of the selected strategy during point cloud preprocessing, PassThrough, and RadiusOutlierRemoval are combined to ensure quality. An actual vehicle is used in complex indoor environment to verify the stability and robustness of HT-Carto. Compared to the Cartographer, Karto, Hector, and GMapping, HT-Carto demonstrates better localization and mapping, it can obtain a more precise trajectory.

Wheel-leg composite robots exhibit robust mobility and exceptional obstacle-crossing capabilities in complex environments. This paper proposes a novel transformable wheel-leg composite structure and presents the design of a wheel-leg composite obstacle-crossing robot, fundamentally configured as a two-wheeled quadruped. The research encompasses a comprehensive analysis of the robot’s overall mechanical structure, a detailed kinematic investigation of its body and obstacle-crossing gait planning, virtual prototype dynamics simulation, and field experimentation. Utilizing advanced modeling software, a 3D model of the robot was established. The kinematic characteristics of the robot in both wheeled and legged modes were thoroughly examined. Specifically, for the legged mode, the Denavit-Hartenberg coordinate system was established, and a detailed kinematic model was analyzed. The obstacle-crossing gait was planned based on the robot’s leg action mechanism. Furthermore, the Lagrangian method was employed to develop a mathematical model for the dynamics of the robot in both wheel-foot modes, allowing for a comprehensive force analysis. To validate the feasibility and rationality of the robot’s obstacle-crossing capabilities under various conditions, extensive simulations and prototype tests were conducted across diverse terrains. The results provide valuable insights and practical guidance for the structural design of wheel-leg composite obstacle-crossing robots, contributing to advancements in this promising field.

Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.