Underwater archaeology is of great significance for historical and cultural transmission and preservation of underwater heritage, but it is also a challenging task. Underwater heritage is located in an environment with high sediment content, objects are mostly buried, and the water is turbid, resulting in some of the features of objects missing or blurred, making it difficult to accurately identify and understand the semantics of various objects in the scene. To tackle these issues, this paper proposes a global enhancement network (GENet) underwater scene parsing method. We introduce adaptive dilated convolution by adding an extra regression layer, which can automatically deduce adaptive dilated coefficients according to the different scene objects. In addition, considering the easy confusion in the process of fuzzy feature classification, an enhancement classification network is proposed to increase the difference between various types of probabilities by reducing the loss function. We verified the validity of the proposed model by conducting numerous experiments on the Underwater Shipwreck Scenes (USS) dataset. We achieve state-of-the-art performance compared to the current state-of-the-art algorithm under three different conditions: conventional, relic semi-buried, and turbidified water quality. The experimental results show that the proposed algorithm performs best in different situations. To verify the generalizability of the proposed algorithm, we conducted comparative experiments on the current publicly available Cityscapes, ADE20K, and the underwater dataset SUIM. The experimental results show that this paper achieves good performance on the public dataset, indicating that the proposed algorithm is generalizable.

In this study, a novel kinematic calibration method is proposed to improve the absolute positioning accuracy of 6R robot. This method can achieve indirect compensation of the 25 parameters of modified Denavit–Hartenberg (MDH). The procedures of the method are threefold. Firstly, the 25-parameter errors model of MDH is initially established. However, only the errors of 10 parameters can be directly compensated in the 25-parameter errors model, since the inverse kinematics algorithm has to meet Pieper criterion. Subsequently, a calibration method is proposed to improve accuracy of the absolute position, which uses the Newton–Raphson method to transform the 25-parameter errors into 10-parameter errors (namely T-10 parameter model). Finally, the errors corresponding to 10 parameters in the T-10 parameters model are identified through the least square method. The calibration performances of T-10 parameters model are comprehensively validated by experimentation on two ER6B-C60 robots and one RS010N robot. After kinematic calibration, the average absolute positioning accuracy of the three robots can be improved by about 90%. The results indicate that the proposed calibration method can achieve more precise absolute positioning accuracy and has a wider range of universality.

The research of parallel mechanism (PM) configuration involves many problems. From topology to configuration, dimensional constraint, etc., how to establish the relationship between topology and configuration with effective methods is a long-term challenge for the configuration design. In this paper, the chemical molecular spatial structure (CMSS) is linked with the configuration of symmetrical parallel mechanism (SPM). Starting from the methane molecule (CH4), a spatial structural topological relation is obtained. Based on graph theory and the spatial structural topological relation, a new expression method with topological graph and its kinematic pair adjacency matrix for spatial SPM is proposed. Then, the expression and analysis for the characteristics of spatial SPM are obtained. Finally, by taking the 3-RPS PM and the 3-RRC PM as examples, the effectiveness and corresponding consistency of the proposed expression method are successfully verified. The proposed new expression method paves the way for the subsequent digital and automated design and analysis of the SPM configuration.

Fiber winding reinforcement is widely used in soft robotic manipulators actuated by pressurized fluids. However, the specific effect of each type of winding on the bending motion of a tubular soft robotics manipulator with three chambers has not been explored widely. We present the development of precise finite element (FE) simulations and investigate the effect of helical fiber winding parameters on the bending motion of a two-degree-of-freedom manipulator with three internal chambers. We first show the development of an FE simulation that optimizes convergence and computational time and precisely matches the behavior of soft robots in practice. Compared to single-chamber robots, simulating three-chamber designs is more challenging due to the complex geometry. We then apply our FE model to simulate all the parameter variations. We show that for helical winding with a constant pitch, the closer the center of a chamber is to the intersection of the windings, the lower the bending stiffness of the chamber is. To minimize bending stiffness variation in different bending directions, the optimal angle between the center of the first chamber and the intersection of the two helical windings are 0° and 12°. Reducing the pitch of the helical windings or using other types of windings (i.e., ring winding or six helical winding) reduces the stiffness variation across different bending directions. The FE simulations are compared with experiments showing that the model can capture complex bending behaviors of the manipulator, even though the estimation tends to be less accurate at higher bending angles.

In this paper, the Hill Valley (HV) approach is applied to the drifting masses or agents in the basic Gravitational Search Algorithm (GSA) for the path planning of humanoid robots. The drift in lighter masses toward the heavier mass creates a localized area, where the probability of obtaining a globally optimal solution is very high. So, the present work is focused on exploiting the area to tackle local optima and provide the best steering angle for the humanoids to navigate. The HV approach is applied to the basic GSA model, at the later stages, to improve the overall computational time and cost. The robustness of the proposed controller was tested in both simulation and experimental environments and compared with the previous research. The results obtained from the proposed controller showed a significant improvement in the overall path length and time taken. Path smoothness was also given equal importance during path planning to ensure stability. The multi-robot navigational scheme was performed using the Dining Philosopher’s model to avoid dynamic collision among the humanoids. The percentage deviation in the results was within the acceptable limits. To further check the effectiveness of the proposed technique, the proposed approach was compared with the vision-based navigation in danger space.

This study presents a novel 4-DOF two-limb gripper mechanism with a simple design that offers high adaptability for different objects. The mechanism integrates a three-finger end effector and employs a 2-DOF driving system in both serial kinematic chains mounted on the base, addressing performance problems caused by moving actuators. First, the architecture of the gripper mechanism is described, and its mobility is verified. Next, the inverse and forward kinematic problems are solved, and the Jacobian matrix is derived to analyze the singularity conditions. The inverse and forward singularity surfaces are plotted. The workspace is investigated using a search method, and two indices, manipulability and dexterity, are studied. The proposed manipulator’s parameters are optimized for improved dexterity. The novel gripper mechanism has high potential for grasping different types of parts within a large workspace, making it a valuable addition to the field of robotics.

In order to solve the problems of low loading capacity and low driving efficiency for the powered exoskeleton, this paper presents a bionic multi-chamber pneumatic actuator based on muscle scale mechanism. Firstly, the bionic muscle scale mechanism and multi-chamber structure design for the novel pneumatic actuator are introduced. Afterward, the driving characteristics of the multi-chamber actuator are analyzed theoretically, including analysis of output force and analysis of energy efficiency. Then, the load matching control strategy for the novel actuator is optimized, and the load matching performance, displacement tracking accuracy, and energy efficiency are studied by simulation. Finally, the prototype of the multi-chamber actuator is developed, and the exoskeleton testing platform is built, experiment and discussion are conducted for the driving characteristics, which realized the high energy efficiency and the feasibility of load matching.

As a solution to soft material manipulation, dual-arm robots that are capable of multi-point grasping have become the latest trend due to their higher capability and efficiency. To explore further development in soft material manipulation and go beyond the hardware limit of dual arms, in this paper the robot platform “Quad-SCARA” that consists of four Selective Compliance Assembly Robot Arm (SCARA) is developed and introduced. With the base of hardware platform, a novel collision avoidance system designed for the motion planning of Quad-SCARA in multi-point grasping state is proposed. This system is inspired by the idea of “Buffered Voronoi Cell (BVC),” an algorithm originally proposed for multi-agent collision avoidance. After appropriate adaptation and new proposition in implementation of BVC, the experiments of folding and spreading a handkerchief are performed, and the results in simulation and robot experiments are presented and discussed for the validation and evaluation of entire system.

Rehabilitation robots help the treatment of diseases by performing cyclic exercises for a long period of time. These exercises must perform movements of the patient’s limbs; thus, the robots are required to be flexible and safe. Among rehabilitation robots, cable robots are widely used due to their unique properties, such as being lightweight and the possibility of being equipped with magnetic hooks to improve both safety and ease of use. However, the elasticity and flexibility of cables result in vibrations of the payload and hooks. In this paper, the forced vibrations due to rehabilitation exercises are studied. Since the previous studies of the authors showed a weak coupling between longitudinal and transverse vibrations, a two-cable planar model for the study of transverse vibrations is developed. The model makes it possible to study the forced transverse vibrations due to both cable motion and robot motion. Stiffness and damping of the patient’s arm are considered. Results show that the cable system exhibits a simple linear behavior when excited by robot motion and a non-linear behavior when excited by cable motion.

Accurate tracking and reconstruction of surgical scenes is a critical enabling technology toward autonomous robotic surgery. In endoscopic examinations, computer vision has provided assistance in many aspects, such as aiding in diagnosis or scene reconstruction. Estimation of camera motion and scene reconstruction from intra-abdominal images are challenging due to irregular illumination and weak texture of endoscopic images. Current surgical 3D perception algorithms for camera and object pose estimation rely on geometric information (e.g., points, lines, and surfaces) obtained from optical images. Unfortunately, standard hand-crafted local features for pose estimation usually do not perform well in laparoscopic environments. In this paper, a novel self-supervised Surgical Perception Stereo Visual Odometer (SPSVO) framework is proposed to accurately estimate endoscopic pose and better assist surgeons in locating and diagnosing lesions. The proposed SPSVO system combines a self-learning feature extraction method and a self-supervised matching procedure to overcome the adverse effects of irregular illumination in endoscopic images. The framework of the proposed SPSVO includes image pre-processing, feature extraction, stereo matching, feature tracking, keyframe selection, and pose graph optimization. The SPSVO can simultaneously associate the appearance of extracted feature points and textural information for fast and accurate feature tracking. A nonlinear pose graph optimization method is adopted to facilitate the backend process. The effectiveness of the proposed SPSVO framework is demonstrated on a public endoscopic dataset, with the obtained root mean square error of trajectory tracking reaching 0.278 to 0.690 mm. The computation speed of the proposed SPSVO system can reach 71ms per frame.

Real-time gait trajectory planning is challenging for legged robots walking on unknown terrain. In this paper, to realize a more efficient and faster motion control of a quadrupedal robot, we propose an optimized gait planning generator (GPG) based on the decision tree (DT) and random forest (RF) model of the robot leg workspace. First, the framework of this embedded GPG and some of the modules associated with it are illustrated. Aiming at the leg workspace model described by DT and RF used in GPG, this paper introduces in detail how to collect the original data needed for training the model and puts forward an Interpolation Labeling with Dilation and Erosion (ILDE) data processing algorithm. After the DT and RF models are trained, we preliminarily evaluate their performance. We then present how these models can be used to predict the location relation between a spatial point and the leg workspace based on its distributional features. The DT model takes only 0.00011 s to process a sample, while the RF model can give the prediction probability. As a complement, the PID inverse kinematic model used in GPG is also mentioned. Finally, the optimized GPG is tested during a real-time single-leg trajectory planning experiment and an unknown terrain recognition simulation of a virtual quadrupedal robot. According to the test results, the GPG shows a remarkable rapidity for processing large-scale data in the gait trajectory planning tasks, and the results can prove it has an application value for quadruped robot control.

In recent years, deep learning-based robotic grasping methods have surpassed analytical methods in grasping performance. Despite the results obtained, most of these methods use only planar grasps due to the high computational cost found in 6D grasps. However, planar grasps have spatial limitations that prevent their applicability in complex environments, such as grasping manufactured objects inside 3D printers. Furthermore, some robotic grasping techniques only generate one feasible grasp per object. However, it is necessary to obtain multiple possible grasps per object because not every grasp generated is kinematically feasible for the robot manipulator or does not collide with other close obstacles. Therefore, a new grasping pipeline is proposed to yield 6D grasps and select a specific object in the environment, preventing collisions with obstacles nearby. The grasping trials are performed in an additive manufacturing unit that has a considerable level of complexity due to the high chance of collision. The experimental results prove that it is possible to achieve a considerable success rate in grasping additive manufactured objects. The UR5 robot arm, Intel Realsense D435 camera, and Robotiq 2F-140 gripper are used to validate the proposed method in real experiments.

Human activity recognition (HAR) is an emerging challenge among researchers. HAR has many possible uses in various fields, including healthcare, sports, and security. Furthermore, there are only a few publicly accessible datasets for classifying and recognizing physical activity in the literature, and these datasets comprise fewer activities. We created and compared our dataset with available datasets, that is, NTU-RGBD, UP-FALL, UR-Fall, WISDM, and UCI HAR. The proposed dataset consists of seven activities: eating, exercise, handshake, situps, vomiting, headache, and walking. The activities were collected from 20 people between the ages of 25 and 40 years using Kinect V2 sensor at 30 FPS. For classification, we use deep learning architectures based on convolutional neural network (CNN) and long short-term memory (LSTM). Additionally, we developed a novel hybrid deep learning model by combining a CNN, a bidirectional LSTM unit, and a fully connected layer for activity identification. The suggested model builds unique guided features using the preprocessed skeleton coordinates and their distinctive geometrical and kinematic aspects. Results from the experiment are contrasted with the performance of stand-alone CNNs, LSTMs, and ConvLSTM. The proposed model’s accuracy of 99.5% surpasses that of CNN, LSTM, and ConvLSTM, which have accuracy rates of 95.76%, 97%, and 98.89%, respectively. Our proposed technique is invariant of stance, speed, individual, clothes, etc. The proposed dataset sample is accessible to the general public.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

In order to complete many complex operations and attain more general-purpose utility, robotic grasp is a necessary skill to master. As the most common essential action of robots in factory and daily life environments, robotic autonomous grasping has a wide range of application prospects and has received much attention from researchers in the past decade. However, the accurate grasp of arbitrary objects in unstructured environments is still a research challenge that has not yet been completely overcome. A complete robotic grasp system usually involves three aspects: grasp detection, grasp planning, and control subsystem. As the first step, identifying the location of the object and generating the grasp pose is the premise of successful grasp, which is conducive to planning the subsequent grasp path and the realization of the entire grasp action. Therefore, this paper conducts a literature review focusing on grasp detection technology and concludes two significant aspects: the analytic and data-driven methods. According to the previous grasp experience of the target object, this paper divides the data-driven methods into the grasp of known and unknown objects. Then it describes in detail the typical grasp detection methods and related characteristics of each classification in the grasp of unknown objects. Finally, current research status and potential research directions in this field are discussed to provide some reference for related research.