The control of dynamic spatial posture for cantilever roadheader is one of the vital problems for intelligent mining, which directly affect the forming quality of cutting tunnel. Therefore, this paper proposed an intelligent optimal combination compensation strategy to adjust the real-time dynamic posture of cantilever roadheader. First, based on the topological structure analysis of cantilever roadheader, the structural loop compensation model for spatial posture deviation was established. Afterward, the principal component analysis (PCA) and multi-objective particle swarm optimization (MPSO) algorithm were applied to improve the analysis speed and accuracy of posture deviation. Finally, parallel dynamic cooperative optimization (PDCO) strategy was combined to achieve the accurate adjusting of posture deviation. The actual experimental and application results indicate that the intelligent optimal combination compensation strategy proposed in the paper can significantly improve the accuracy of the cutting tunnel. The intelligent optimal compensation strategy proposed in this paper transforms the transient spatial posture deviation into structural loop compensation, and implements by parallel cooperative strategy, finally to realize the fast analysis and efficient implementation of spatial dynamic posture deviation for cantilever roadheader during cutting process. The work of this paper provides an effective reference for intelligent deep and remote underground mining, and it can also be applied to effective control of dynamic spatial posture for intelligent engineering machinery products.

The aim of this work is to design and model a novel modular bionic soft robot for crawling and crossing obstacles. The modular bionic soft robot is composed of several serial driving soft modules, each module is composed of two parallel soft actuators. By analyzing the influence of working pressure and manufacturing size on the stiffness of the modular bionic soft robot, the nonlinear variable stiffness model of the modular bionic soft robot is established. Based on this model, the spatial states and design parameters of the modular bionic soft robot are discussed when the modular bionic soft robot can pass through the obstacle. Experiments show that when the inflation air pressure of the modular bionic soft robot is 70 kPa, its speed can reach 7.89 mm/s and the height of obstacles passed by it can reach 42.8 mm. The feasibility of the proposed modular bionic soft robot and nonlinear variable stiffness model is verified by locomotion experiments.

Soft crawling robots have been significantly studied in recent decades. However, moving in amphibious environment, high payload capability, and passing through complex ground have always been challenges for soft crawling robots. For these problems, this article presents an amphibious soft-rigid wheeled crawling robot (SRWCR) consists of a soft-rigid body actuated by two soft pneumatic actuators (SPAs), four wheels, and four annular soft bladders (ASBs) as brakes. By programming the actuation sequences of the two SPAs and four ASBs, SRWCR can achieve two basic modes of locomotion: linear motion and turning. Based on the energy conservation law, we have developed analytical models to interpret the static actuation performance of SPA, including linear and bending deformations. Furthermore, with the help of fast response and waterproof of SPA and ASB, SRWCR can achieve a linear speed of 14.97 mm/s, a turning speed of 5.63°/s, and an underwater locomotion speed of 13 mm/s, which demonstrates the excellent locomotion performance of SRWCR in amphibious environment. In addition, SRWCR can also achieve multiple impressive functions, including carrying a payload of 2 kg at the moving speed of 11.18 mm/s, passing through various complex ground such as the grass ground and sand ground, and so on, obstacle navigation in confined space. Compared with the existing soft crawling robots, with the help of the soft-rigid body and wheeled structure, SRWCR has the best payload and passing capability, which indicates the potential advantage of SRWCR in the design of functional robots.

Climbing robots have broad application prospects in aerospace equipment inspection, forest farm monitoring, and pipeline maintenance. Different types of climbing robots in existing research have different advantages. However, the self-adaptability and stability have not been achieved at the same time. In order to realize the self-adaptability of holding and climbing stability, this work proposes a new type of climbing robot under the premise of minimizing the driving source. The robot realizes stable multifinger holding and wheeled movement through two motors. At the same time, the robot has two different working modes, namely pole climbing and ground crawling. The holding adaptability and climbing stability are realized by underactuated holding mechanism and model reference adaptive controller (MRAC). On the basis of model design and parameter analysis, a prototype of the climbing robot is built. Experiments prove that the proposed climbing robot has the ability to stably climb poles of different shapes. The holding and climbing stability, self-adaptability, and climbing and crawling speed of the proposed climbing robot are verified by experiments.

In this paper, the inverse kinematics (IK) of redundant manipulators is presented and studied, where the performance of end-effector path planning is guaranteed. A new Jacobian pseudoinverse (JP)-based IK method is proposed and studied using a typical numerical difference rule to discretize the existing IK method based on JP. The proposed method is depicted in a discrete-time form and is theoretically proven to exhibit great performance in the IK of redundant manipulators. A discrete-time repetitive path planning (DTRPP) scheme and a discrete-time obstacle avoidance (DTOA) scheme are developed for redundant manipulators using the proposed method. Comparative simulations are conducted on a universal robot manipulator and a PA10 robot manipulator to validate the effectiveness and superior performance of the DTRPP scheme, the DTOA scheme, and the proposed JP-based IK method.

The paper deals with the workspace-based optimization of a novel humanoid robotic arm. The eight-degree-of-freedom hybrid manipulator that conforms to the kinematics characteristics of the human arm is briefly introduced. According to the structural features of this mechanism and the requirements of tasks in the complex environment, the workspace is divided into three parts, the orientation space of the humanoid shoulder joint, the position space of the humanoid elbow joint, and the active orientation space of the end-moving platform. Moreover, a multi-parameter planar model is proposed for the optimization problem with multidimensional parameters and highly nonlinear constraints. Based on the visualized optimization result, the coupling effect of each parameter on the corresponding workspace is clearly presented. Considering the compactness and the processing and assembling technology of this mechanism, a set of structural parameters satisfying the workspace-based optimization objective is obtained. Simulation results show that the corresponding workspace of the three parts has increased significantly by the factor of 1.45, 1.68, and 1.3, respectively.

This paper presents a numerical and experimental validation of ExoFing, a two-degrees-of-freedom finger mechanism exoskeleton. The main functionalities of this device are investigated by focusing on its kinematic model and by computing its main operation characteristics via numerical simulations. Experimental tests are designed and carried out for validating both the engineering feasibility and effectiveness of the ExoFing system aiming at achieving a human index finger motion assistance with cost-oriented and user-friendly features.

Operation of tools has long been studied in robotics. Although appropriate hold of the tool by robots is the base of successful tool operation, it is not with ease especially for tools with complicated shape. In this paper, an assist system for a four-limbed robot is proposed for remote operation of reaching and grasping electric drills using deep reinforcement learning. Through comparative evaluation experiments, the increase of success rate for reaching and grasping is verified and the decrease in both physical and mental workload of the operator is also validated by the index of NASA-TLX.

The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.

Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.

Efficiently solving inverse kinematics (IK) of robot manipulators with offset wrists remains a challenge in robotics due to noncompliance with Pieper criteria. In this paper, an improved method to solve the IK for 6-DOF robot manipulators with offset wrists is proposed. This method is based on the Newton iteration technique, but it does not require a selection of initial estimation of joint variables. The solution is divided into two parts: the first part is to reconstruct a simplified structure with analytical IK solution, and the second part is to obtain a numerical solution by iteration. Further, a robot manipulator HSR-BR606 with an offset wrist is used as an example to specifically elaborate the mathematical procedure of the method and to investigate the algorithm in terms of accuracy, efficiency, and application of motion planning. A comparative experiment is conducted with a typical IK algorithm, which demonstrates a higher accuracy and shorter calculation time of the proposed method. The mean calculation time for a single IK solution required for this algorithm is only 4% of the comparison algorithm.

This paper proposes an iterative algorithm to solve the inverse displacement for a hyper-redundant elephant’s trunk robot (HRETR). In this algorithm, each parallel module is regarded as a geometric line segment and point model. According to the forward approximation and inverse pose adjustment principles, the iteration process can be divided into forward and backward iteration. This iterative algorithm transforms the inverse displacement problem of the HRETR into the parallel module’s inverse displacement problem. Considering the mechanical joint constraints, multiple iterations are carried out to ensure that the robot satisfies the required position error. Simulation results show that the algorithm is effective in solving the inverse displacement problem of HRETR.

The purpose is to define the range of feasible speeds for two walking motions for a particular planar biped robot, which differ in the definition of their finite-time double support phases. For each speed, these two walking motions are numerically obtained by using a parametric optimization algorithm, regarding a sthenic criterion. Results allow us to define the range of allowable speeds for each walking. One result is that the first gait is less consuming in energy for moderate to fast velocity with respect to the second one, while the second gait is more efficient for low walking velocity.

This paper proposes a kinematic obstacle avoidance algorithm for Space hyper-redundant manipulators, and its basic idea is to use a static and a dynamic curve to constrain the macroshape of the manipulators simultaneously. The static curve is constructed based on a traditional rapidly exploring random tree algorithm, and a backbone curve is utilized as the dynamic curve. For these two curves, two novel shape control methods are proposed to accomplish the shape constraining process. Finally, we verify the reliability and effectiveness of our algorithm through simulations.

The trajectory-planning method for a novel 4-degree-of-freedom high-speed parallel robot is studied herein. The robot’s motion mechanism adopts RR(SS)2 as branch chains and has a single moving platform structure. Compared with a double moving platform structure, the proposed parallel robot has better acceleration and deceleration performance since the mass of its moving platform is lighter. An inverse kinematics model of the mechanism is established, and the corresponding relationship between the motion parameters of the end-moving platform and the active arm with three end-motion laws is obtained, followed by the optimization of the motion laws by considering the motion laws’ duration and stability. A Lamé curve is used to transition the right-angled part of the traditional gate trajectory, and the parameters of the Lamé curve are optimized to achieve the shortest movement time and minimum acceleration peak. A method for solving Lamé curve trajectory interpolation points based on deduplication optimization is proposed, and a grasping frequency experiment is conducted on a robot prototype. Results show that the grasping frequency of the optimized Lamé curve prototype can be increased to 147 times/min, and its work efficiency is 54.7% higher than that obtained using the traditional Adept gate-shaped trajectory.

Path planning has been widely investigated by many researchers and engineers for its extensive applications in the real world. In this paper, a biharmonic radial basis potential function (BRBPF) representation is proposed to construct navigation fields in 2D maps with obstacles, and it therefore can guide and design a path joining given start and goal positions with obstacle avoidance. We construct BRBPF by solving a biharmonic equation associated with distance-related boundary conditions using radial basis functions (RBFs). In this way, invalid gradients calculated by finite difference methods in large size grids can be preventable. Furthermore, paths constructed by BRBPF are smoother than paths constructed by harmonic potential functions and other methods, and plenty of experimental results demonstrate that the proposed method is valid and effective.

This research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.

A mathematical model is proposed for a revolute joint mechanism with an n-degree of freedom (DOF). The matrix approach is used for finding the relation between two consecutive links to determine desired link parameters such as position, velocity and acceleration using the forward kinematic approach. The matrix approach was confirmed for a proposed 10 DOF revolute type (R-type) human upper limb model with servo motors at each joint. Two DOFs are considered each at shoulder, elbow and wrist joint, followed by four DOF for the fingers. Two DOFs were considered for metacarpophalangeal (mcp) and one DOF each for proximal interphalangeal (pip) and distal interphalangeal (dip) joints. MATLAB script function was used to evaluate the mathematical model for determining kinematic parameters for all the proposed human upper limb model joints. The simplified method for kinematic analysis proposed in this paper will further simplify the dynamic modeling of any mechanism for determining joint torques and hence, easy to design control system for joint movements.

Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

An unexpected path reversal is discovered for a serial robot with an offset-axis wrist over a finite range of proximity to the wrist singularity. This is replicated by a kinematic model. A prior spherical-wrist method transits the singularity with limited joint rate and acceleration under a constant rate of tool traversal. Accurate position is maintained by controlling a small deviation in orientation. Extensions to the method for an offset wrist (1) find the least-maximum deviation, (2) identify and locate where a path reversal occurs, and (3) use this point to control step size in a high-order predictor-correction path following procedure.