This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.

In this paper, a method of planning the expanded S-curve trajectory of robotic manipulators is proposed to minimize the execution time as well as to achieve the smoother trajectory generation in the deceleration stage for point-to-point motions. An asymmetric parameter is added to the piecewise sigmoid function for an improved jerk profile. This asymmetric profile is continuous and infinitely differentiable. Based on this profile, two analytical algorithms are presented. One is applied to determine the suitable time intervals of trajectory satisfying the time optimality under the kinematic constraints, and the other is to determine the asymmetric parameter generating the minimum execution time. Also, the calculation procedure for the time-scaled synchronization for all joints is given to decrease unnecessary loads onto the actuators. The velocity, acceleration, jerk and snap (the derivative of jerk) of the joints and the end-effector are equal to zero at two end points of motion. The simulation results through 3 DOF and 6 DOF robotic manipulators show that our approach reduces the jerk and snap of the deceleration stage effectively while decreasing the total execution time. Also, the analysis for a single DOF mass-spring-damper system indicates that the residual vibration could be reduced to 10% more than the benchmark techniques in case velocity, acceleration and jerk are limited to 1.24 m/s, 6 m/s2 and 80 m/s3, respectively and displacement is set to 0.8m. These results manifest that the performance of reducing residual vibrations is good and demonstrate an important characteristic of the proposed profile suitable for point-to-point motion.

Visual odometry (VO) is a key technology for estimating camera motion from captured images. In this paper, we propose a novel RGB-D visual odometry by constructing and matching features at the superpixel level that represents better adaptability in different environments than state-of-the-art solutions. Superpixels are content-sensitive and perform well in information aggregation. They could thus characterize the complexity of the environment. Firstly, we designed the superpixel-based feature SegPatch and its corresponding 3D representation MapPatch. By using the neighboring information, SegPatch robustly represents its distinctiveness in various environments with different texture densities. Due to the inclusion of depth measurement, the MapPatch constructs the scene structurally. Then, the distance between SegPatches is defined to characterize the regional similarity. We use the graph search method in scale space for searching and matching. As a result, the accuracy and efficiency of matching process are improved. Additionally, we minimize the reprojection error between the matched SegPatches and estimate camera poses through all these correspondences. Our proposed VO is evaluated on the TUM dataset both quantitatively and qualitatively, showing good balance to adapt to the environment under different realistic conditions.

Reinforcement learning (RL) has been successfully applied to a wealth of robot manipulation tasks and continuous control problems. However, it is still limited to industrial applications and suffers from three major challenges: sample inefficiency, real data collection, and the gap between simulator and reality. In this paper, we focus on the practical application of RL for robot assembly in the real world. We apply enlightenment learning to improve the proximal policy optimization, an on-policy model-free actor-critic reinforcement learning algorithm, to train an agent in Cartesian space using the proprioceptive information. We introduce enlightenment learning incorporated via pretraining, which is beneficial to reduce the cost of policy training and improve the effectiveness of the policy. A human-like assembly trajectory is generated through a two-step method with segmenting objects by locations and iterative closest point for pretraining. We also design a sim-to-real controller to correct the error while transferring to reality. We set up the environment in the MuJoCo simulator and demonstrated the proposed method on the recently established The National Institute of Standards and Technology (NIST) gear assembly benchmark. The paper introduces a unique framework that enables a robot to learn assembly tasks efficiently using limited real-world samples by leveraging simulations and visual demonstrations. The comparative experiment results indicate that our approach surpasses other baseline methods in terms of training speed, success rate, and efficiency.

The manufacturing of the X-shaped tip of prestressed centrifugal concrete piles is nowadays done half automatically by combining the manual worker and the automatic welding robot. To make this welding process full automatically, the welding seam tracking algorithm is considered. There are many types of sensors that can be used to detect the welding seam such as vision sensor, laser vision sensor, arc sensor, or touch sensor. Each type of sensor has its advantages and disadvantages. In this paper, an algorithm for welding seam tracking using laser distance sensor is proposed. Firstly, the fundamental mathematics theory of the algorithm is presented. Next, the positioning table system supports the procedure is designed and manufactured. The object of this research is the fillet joint because of the characteristics of the X-shaped tip of the concrete piles. This paper proposes a new method to determine the welding trajectory of the tip using laser distance sensor. After that, the experimental results are received to verify the proposed idea. Finally, the improved proposal of the algorithm is considered to increase the accuracy of the suggested algorithm.

In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.

In view of the fact that the current research on active and passive rehabilitation training of lower limbs is mainly based on the analysis of exoskeleton prototype and the lack of analysis of the actual movement law of limbs, the human-machine coupling dynamic characteristics for active rehabilitation training of lower limbs are studied. In this paper, the forward and inverse kinematics are solved on the basis of innovatively integrating the lower limb and rehabilitation prototype into a human-machine integration system and equivalent to a five-bar mechanism. According to the constraint relationship of hip joint, knee joint and ankle joint, the Lagrange dynamic equation and simulation model of five-bar mechanism under the constraint of human physiological joint motion are constructed, and the simulation problem of closed-loop five-bar mechanism is solved. The joint angle experimental system was built to carry out rehabilitation training experiments to analyze the relationship between lower limb error and height, weight and BMI, and then, a personalized training planning method suitable for people with different lower limb sizes was proposed. The reliability of the method is proved by experiments. Therefore, we can obtain the law of limb movement on the basis of traditional rehabilitation training, appropriately reduce the training speed or reduce the man-machine position distance and reduce the training speed or increase the man-machine distance to reduce the error to obtain the range of motion angle closer to the theory of hip joint and knee joint respectively, so as to achieve better rehabilitation.

This article presents a detailed examination of circular target localization techniques for measuring robot pose and performing online pose correction. The investigated target localization methods include centroiding, ellipse fitting with point data and gradient information, and ellipse fitting methods with augmented and corrected input data. The performance of each method is evaluated in terms of accuracy and precision of measurements through experimental comparison with a laser tracker. This study provides technical and practical insights for selecting an appropriate target localization method in robotic applications. It also introduces a vision-based solution for robot relative error correction, comprising the calibration procedure and a closed-loop control with a proportional–integral-derivative controller for pose correction. Results show enhanced accuracy in robot positioning relative to workpiece, highlighting the effectiveness of the proposed solution in robotic drilling applications.

Bag manipulation through robots is complex and challenging due to the deformability of the bag. Based on the dynamic manipulation strategy, we propose a new framework, ShakingBot, for the bagging tasks. ShakingBot utilizes a perception module to identify the key region of the plastic bag from arbitrary initial configurations. According to the segmentation, ShakingBot iteratively executes a novel set of actions, including Bag Adjustment, Dual-arm Shaking, and One-arm Holding, to open the bag. The dynamic action, Dual-arm Shaking, can effectively open the bag without the need to take into account the crumpled configuration. Then, the robot inserts the items and lifts the bag for transport. We perform our method on a dual-arm robot and achieve a success rate of 21/33 for inserting at least one item across various initial bag configurations. In this work, we demonstrate the performance of dynamic shaking action compared to the quasi-static manipulation in the bagging task. We also show that our method generalizes to variations despite the bag’s size, pattern, and color. Supplementary material is available at https://github.com/zhangxiaozhier/ShakingBot.

In this paper, an unmanned bicycle (UB) with a reaction wheel is designed, and a second-order mathematical model with uncertainty is established. In order to achieve excellent balancing performance of the UB system, an adaptive controller is designed, which is composed of nominal feedback control, compensating control using extreme learning machine observer and reaching control via integral terminal sliding mode (ITSM) and barrier function (BF)-based adaptive law. Owing to the features of BF-based ITSM (BFITSM), not only any uncertainty or disturbance upper bound is not needed any longer but also the finite-time convergence of the closed-loop system can be ensured with a predefined error bound. Moreover, the BF-based control gain can be adaptively adjusted according to the update of the lumped uncertainty such that the overestimation is removed. The stability analysis of the closed-loop system is given according to Lyapunov theory. Comparable experimental results on an actual UB are carried out to validate the superior balancing performance of the proposed controller.

This paper presents a kinematics modeling and hybrid motion planning framework for wheeled-legged rovers. It is a unified solution for wheeled-legged rovers to traverse multiple challenging terrains using hybrid locomotion. A kinematic model is first established to describe the rover’s motions. Then, a hybrid motion planning framework is proposed to determine the rover’s gait patterns and parameterize the legs’ and the body’s trajectories. Furthermore, an optimization algorithm based on B-spline is utilized to minimize the motors’ energy dissipation and generate smooth trajectories. The wheeled and legged hybridization allows the rover for faster locomotion while maintaining high stability. Besides, it also improves the rover’s ability to overcome obstacles. Prototype experiments are carried out in more complex environments to verify the rover’s flexibility and maneuverability to traverse irregular terrains. The proposed algorithm reduces the swing amplitude by 83.3% compared to purely legged locomotion.

Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

In order to solve the problem of the insufficient adaptability of the current small- and medium-sized casting sorting robot gripper, we have designed a casting sorting robot bionic gripper with rigid–flexible coupling structures based on the robot topology theory. The second-order Yeoh model was used to statically model the clamping belt in the gripper to derive the relationship between the external input air pressure and the bending angle of the driving layer, and the feasibility of multiangle bending of the driving layer was verified by finite element analysis. The maximum gripping diameter of the gripper is 140 mm, and in order to test the adaptive gripping ability of the gripper, a prototype of the casting sorting robot gripper is prepared, and the pneumatic control system and human–machine interface of the gripper are designed. After several experimental analyses, the designed casting sorting robot gripper is characterized by strong adaptability and high robustness, with a maximum load capacity of 930 g and a maximum wrap angle of 296°, which can complete the gripping operation within 1 s, and the comprehensive gripping success rate reaches 96.4%. The casting sorting robot gripper designed in the paper can provide a reference for the design and optimization of various types of shaped workpiece gripping manipulators.

Although several studies have revealed that fractional order controllers usually outperform conventional integer-order control solutions, fractional order controllers are not yet widely applied in industrial applications due to their complex mathematical background. In this paper, further improvements of a simple weighted sum feedback design are introduced that imitates the behavior of a fractional order controller but is free from its various formal restrictions. The proposed control solution has the main characteristics of a fractional order controller, such as finite memory length, excellent transient response with no overshoot and robust behavior, but it is placed into a much simpler mathematical framework. In the current paper, a simple derivative term was incorporated in the design which made the controller’s output more stable by completely eliminating output chattering. The proposed control method was developed for a general second-order system. It was tested in a fixed point iteration-based adaptive control scenario, through simulations using a robotic example and on experimental basis as well, utilizing a simple one-degree-of-freedom electromechanical system. The presented experiments are the first systematic investigations of the fixed point iteration-based adaptive control method.

Closed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

This paper introduces a simplified matrix method for balancing forces and moments in planar parallel manipulators. The method resorts to Newton’s second law and the concept of angular momentum vector, yet it is not necessary to perform the velocity and acceleration analyses, tasks that were normally unavoidable in seminal contributions. With the introduction of natural matrices, the proposed balancing method is independent of the time and the trajectory generated by the moving links of parallel manipulators. The effectiveness of the method is exemplified by balancing two planar parallel manipulators.

A negative pressure wall-climbing robot is a special robot for climbing vertical walls, which is widely used in construction, petrochemicals, nuclear energy, shipbuilding, and other industries. The mobility and adhesion of the wheel-track wall-climbing robot with steering-straight mode are significantly decreased on the cylindrical wall, especially during steering. The reason is that the suction chamber may separate from the wall and the required driving force for movement increases, during steering. In this paper, a negative pressure wall-climbing robot with omnidirectional movement mode is developed. By introducing a compliant adjusting suction mechanism and omni-belt wheels, an omnidirectional movement mode is formed instead of the steering-straight mode, and the performances of adhesion and mobility are improved. We establish the safety adhesion model for the robot on a cylindrical wall and obtain the safety adhesion forces. We designed and manufactured an experimental prototype based on the analysis. Experiments showed that the robot has the ability of full maneuverability in cylindrical walls.

The dual-user training system is essential for fostering motor skill learning, particularly in complex operations. However, the challenge lies in the optimal tradeoff between trainee ability and engagement level. To address this problem, we propose an intelligent agent that coordinates trainees’ control authority during real task engagement to ensure task safety during training. Our approach avoids the need for manually set control authority by expert supervision. At the same time, it does not rely on pre-modeling the trainee’s skill development. The intelligent agent uses a deep reinforcement learning (DRL) algorithm based on trainee performance to adjust adaptive engagement during the training process. Our investigation aims to provide reasonable engagement for trainees to improve their skills while ensuring task safety. Our results demonstrate that this system can seek the policy to maximize trainee participation while guaranteeing task safety.

Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

In this paper, an online adaptive super twisting sliding mode controller is proposed for a non-linear system. The adaptive controller has been designed in order to deal with the unknown dynamic uncertainties and give the best trajectory tracking. The adaptation is based on an optimal Particle Swarm Optimization (PSO) algorithm whose goal is online tuning the parameters through focusing on decreasing the objective function. The novelty of this study is online handling parameters setting in the conventional super twisting algorithm, bypass heavy offline calculation, and also avoid the instability and abrupt changing of the controller’s parameters for better actuators lifetime. This novel approach has been applied on an upper limb exoskeleton robot for arm rehabilitation. Despite the changes of the dynamic model of the system which defers from one patient to another due to the direct interactions between the wearer and the exoskeleton, this control technique preserves its robustness with respect to bounded external disturbances. The effectiveness of the proposed adaptive controller has been proved in simulation and then in real-time experiment with two human subjects. A comparison between the proposed approach and classic super twisting algorithm has been conducted. The obtained results show the performance and efficiency of the proposed controller.