Compliant interaction between robots and the environment is crucial for completing contact-rich tasks. However, obtaining and implementing optimal interaction behavior in complex unknown environments remains a challenge. This article develops a hybrid impedance and admittance control (HIAC) scheme for robots subjected to the second-order unknown environment. To obtain the second-order target impedance model that represents the optimal interaction behavior without the accurate environment dynamics and acceleration feedback, an impedance adaptation method with virtual inertia is proposed. Since impedance control and admittance control have complementary structures and result in unsatisfactory performance in a wide range of environmental stiffness due to their fixed causality, a hybrid system framework suitable for the second-order environment is proposed to generate a series of intermediate controllers which interpolate between the responses of impedance and admittance controls by using a switching controller and adjusting its switching duty cycle. In addition, the optimal intermediate controller is selected using a mapping of the optimal duty cycle to provide the optimal implementation performance for the target impedance model. The proposed HIAC scheme can achieve the desired interaction and impedance implementation performance while ensuring system stability. Simulation and experimental studies are performed to verify the effectiveness of our scheme with a 2-DOF manipulator and a 7-DOF Franka EMIKA panda robot, respectively.

Autonomous underwater vehicles (AUVs) have played a pivotal role in advancing ocean exploration and exploitation. However, traditional AUVs face limitations when executing missions at minimal or near-zero forward velocities due to the ineffectiveness of their control surfaces, considerably constraining their potential applications. To address this challenge, this paper introduces an innovative vectored thruster system based on a 3RRUR parallel manipulator tailored for micro-sized AUVs. The incorporation of a vectored thruster enhances the performance of micro-sized AUVs when operating at minimal and low forward speeds. A comprehensive exploration of the kinematics of the thrust-vectoring mechanism has been undertaken through theoretical analysis and experimental validation. The findings from theoretical analysis and experimental confirmation unequivocally affirm the feasibility of the devised thrust-vectoring mechanism. The precise control of the vector device is studied using Physics-informed Neural Network and Model Predictive Control (PINN-MPC). Through the adoption of this pioneering thrust-vectoring mechanism rooted in the 3RRUR parallel manipulator, AUVs can efficiently and effectively generate the requisite motion for thrust-vectoring propulsion, overcoming the limitations of traditional AUVs and expanding their potential applications across various domains.

In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.

This paper addresses the problem of controlling multiple unmanned aerial vehicles (UAVs) cooperating in a formation to carry out a complex task such as surface inspection. We first use the virtual leader-follower model to determine the topology and trajectory of the formation. A double-loop control system combining backstepping and sliding mode control techniques is then designed for the UAVs to track the trajectory. A radial basis function neural network capable of estimating external disturbances is developed to enhance the robustness of the controller. The stability of the controller is proven by using the Lyapunov theorem. A number of comparisons and software-in-the-loop tests have been conducted to evaluate the performance of the proposed controller. The results show that our controller not only outperforms other state-of-the-art controllers but is also sufficient for complex tasks of UAVs such as collecting surface data for inspection. The source code of our controller can be found at https://github.com/duynamrcv/rbf_bsmc.

Hyper-redundant cable-driven manipulators (CDMs) are widely used for operations in confined spaces due to their slender bodies and multiple degrees of freedom. Most research focuses on their path following but not path planning. This work investigates a deep deterministic policy gradient (DDPG)-based path-planning algorithm for CDMs in multi-obstacle environments. To plan passable paths under many constraints, a DDPG algorithm is modified according to features of CDMs. To improve adaptability of planned paths, a specialized reward function is newly designed. In this function, such factors as smoothness, arrival time and distance are taken into account. Results of simulations and physical experiments are presented to demonstrate the performances of the proposed methods for planning paths of CDMs.

This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.

Soft robotics is rapidly advancing, particularly in medical device applications. A particular miniaturized manipulator design that offers high dexterity, multiple degrees-of-freedom, and better lateral force rendering than competing designs, has great potential for minimally invasive surgery. However, it faces challenges such as the tendency to suddenly and unpredictably deviate in bending plane orientation at higher pressures. In this work, we identified the cause of this deviation as the buckling of the partition wall and proposed design alternatives along with their manufacturing process to address the problem without compromising the original design features. In both simulation and experiment, the novel design managed to achieve a better bending performance in terms of stiffness and reduced deviation of the bending plane. We also developed an artificial neural network-based inverse kinematics model to further improve the performance of the prototype during vectorization. This approach yielded mean absolute errors in orientation of the bending plane below $5^{\circ }$.

Physically compliant actuator brings significant benefits to robots in terms of environmental adaptability, human–robot interaction, and energy efficiency as the introduction of the inherent compliance. However, this inherent compliance also limits the force and position control performance of the actuator system due to the induced oscillations and decreased mechanical bandwidth. To solve this problem, we first investigate the dynamic effects of implementing variable physical damping into a compliant actuator. Following this, we propose a structural scheme that integrates a variable damping element in parallel to a conventional series elastic actuator. A damping regulation algorithm is then developed for the parallel spring-damping actuator (PSDA) to tune the dynamic performance of the system while remaining sufficient compliance. Experimental results show that the PSDA offers better stability and dynamic capability in the force and position control by generating appropriate damping levels.

We present the design and deployment of a capsule endoscope via external electromagnets for locomotion in large volumes alongside its digital twin implementation based on interval type-2 fuzzy logic systems (IT2-FLSs). To perform locomotion, we developed an external mechanism comprising five external electromagnets on a two-dimensional translational platform that is to be placed underneath the patients’ bed and integrated multiple Neodymium magnets into the capsule. The interaction between the central bottom external electromagnet and the internal magnet forms a fixed body frame at the capsule center, allowing rotation. The interaction between the external electromagnets and the two internal magnets results in rotation. The elevation of the capsule is accomplished due to the interaction between the upper external electromagnet and the internal magnets. Through simulations, we model the capsule rotation as a function of torque and drive voltages. We validated the proposed locomotion approach experimentally and observed that the results are highly nonlinear and uncertain. Thus, we define a regression problem in which IT2-FLSs, capable of representing nonlinearity and uncertainty, are learned. To verify the proposed locomotion approach and test the IT2-FLS, we leverage our experimental effort to a stomach phantom and finally to an ex vivo bovine stomach. The experimental results validate the locomotion capability and show that the IT2-FLS can capture uncertainties while resulting in satisfactory prediction performance. To showcase the benefit in a clinical scenario, we present a digital twin implementation of the proposed approach in a virtual environment that can link physical and virtual worlds in real time.

This paper presents the design, modeling, and control of a novel soft-rigid knee joint robot (SR-KR) for assisting motion. SR-KR is proposed to assist patients with knee joint injuries conducting gait training and completing walking movements. SR-KR consists of a novel soft-rigid bidirectional curl actuator, a thigh clamping structure, and a crus clamping structure. The actuating part of SR-KR is composed of soft materials, which ensures the wearing comfort and safety, while the wearing parts contain rigid structure, which ensures the efficient transmission of torque. The bending deformation model of SR-KR is established, which reveal the relationship among SR-KR’s bending curvature, working pressure, and output torque. Experiments show that SR-KR can provide more than 26.3 Nm torque for knee joint motion in human gait range. A double closed loop servo control system including attitude servo and pressure servo is built to better apply SR-KR. Mechanical property test, trajectory-driven test, and lower limb wearing test have been conducted, which show that SR-KR has ability to assist in lower limb motion and has potential in the fields of rehabilitation and human enhancement.

Inverse kinematics of robot is the basis of robot assembly, which directly determines the pose of robot. Because the traditional inverse solution algorithm is limited by the robot topology structure, singular pose and inverse solution accuracy, it affects the use of robots. In order to solve the above problems, an improved particle swarm optimization (PSO) algorithm is proposed to solve the inverse problem of robot. This algorithm initializes the particle population based on joint angle limitations, accelerating the convergence speed of the algorithm. In order to avoid falling into local optima and premature convergence, we have proposed a nonlinear weight strategy to update the speed and position of particles, enhancing the algorithm’s search ability, in addition introducing a penalty function to eliminate particles exceeding joint limits. Finally, the positions of common points and singular points are selected on PUMA 560 robot and redundant robot for inverse kinematics simulation verification. The results show that, compared with other algorithms, the improved PSO algorithm has higher convergence accuracy and better convergence speed in solving the inverse solution, and the algorithm has certain universality, which provides a new solution for the inverse kinematics solution of the assembly robot.

A deployable manipulator has the characteristics of a small installation space and a large workspace, which has great application prospects in small unmanned platforms. Most existing deployable manipulators are designed based on rigid links, whose complexity and mass inevitably increase sharply with increasing numbers of rigid links and joints. Inspired by the remarkable properties of tape springs, this paper proposes novel deployable parallel tape-spring manipulators with low mass, simple mechanics, and a high deployed-to-folded ratio. First, a double C-shaped tape spring is presented to improve the stability of the structure. The combined fixed drive component (CFDC) and combined mobile drive component (CMDC) are designed. Then, novel 2-DOF and 3-DOF deployable translational parallel manipulators are proposed based on the CFDC and CMDC, and their degrees-of-freedom (DOFs), kinematics, and stability are analyzed. The coiled tape spring is regarded as an Archimedean spiral, which can significantly improve the accuracy of kinematic analysis. The correction coefficient of the Euler formula is obtained by comparison with simulation results and experimental results. Furthermore, the stability spaces of the 2-DOF and 3-DOF deployable parallel manipulators are given. Finally, a prototype is fabricated, and experiments are conducted to validate the proposed design and analysis.

Hyper-redundant robots have good prospects for applications in confined space due to their high flexibility and slim body size. However, the super-redundant structure brings great challenges for its inverse kinematics with shape constraints. Unfortunately, traditional Jacobian pseudo-inverse-based inverse kinematics method and forward and backward reaching inverse kinematics (FABRIK) method are difficult to constrain the arm shape and realize trajectory tracking in confined spaces. To solve this problem, we propose a shape-controllable FABRIK method to satisfy the given path and shape constraints. Firstly, the kinematic model of the hyper-redundant robot is established, and the canonical FABRIK method is introduced. Based on the preliminary works, the single-layer improved FABRIK method is developed to solve the position and pointing inverse kinematics considering path environment and joint angle constraints instead of two-layer geometric iterations. For tracking the desired end roll angles, the polygonal virtual arm is designed. The real arm roll angle is achieved by controlling its winding on the virtual arm. In this way, the shape can be controlled. Finally, we compare the proposed method with other three approaches by simulations. Results show that the proposed method is more efficient and the arm shape is controllable.

The reconfigurable mechanisms can satisfy the requirements of changing environments, working conditions, and tasks on the function and performance of the mechanism and can be applied to machine tool manufacturing, space detection, etc. Inspired by the single-vertex fivefold origami pattern, a new reconfigurable parallel mechanism is proposed in this paper, which has special singular positions and stable motion due to replicating the stabilizing kinematic properties of origami. Through analyzing the topologic change of the folding process of the pattern and treating it as a reconfigurable joint, a new reconfigurable parallel mechanism with 3, 4, 5, or 6 degrees of freedom is obtained. Then, the kinematics solution, workspace, and singularity of the mechanism are calculated. The results indicate that the singular configuration of the origami-derived reconfigurable parallel mechanism is mainly located in a special plane, and the scope of the workspace is still large after the configuration change. The mechanism has the potential to adapt to multiple tasks and working conditions through the conversion among different configurations by folding reconfigurable joints on the branch chain.

This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

The authors have studied models and control methods for legged robots without having active ankle joints that can not only walk efficiently but also stop and developed a method for generating a gait that starts from an upright stationary state and returns to the same state in one step for a simple walker with one control input. It was clarified, however, that achieving a perfect upright stationary state including zero dynamics is impossible. Based on the observation, in this paper we propose a novel robotic walker with parallel linkage legs that can return to a perfect stationary standing posture in one step while simultaneously controlling the stance-leg motion and zero-moment point (ZMP) using two control inputs. First, we introduce a model of a planar walker that consists of two eight-legged rimless wheels, a body frame, a reaction wheel, and massless rods and describe the system dynamics. Second, we consider two target control conditions; one is control of the stance-leg motion, and the other is control of the ZMP to stabilize zero dynamics. We then determine the control input based on the two conditions with the target control period derived from the linearized model and consider adding a sinusoidal control input with an offset to correct the resultant terminal state of the reaction wheel. The validity of the proposed method is investigated through numerical simulations.

The tribological behavior can be informative about the incipient faults of robot manipulators. This study explores the evolution of friction characteristics from cold start to thermal equilibrium through a series of steady-state friction experiments. Based on these experimental observations, a friction-based fault diagnosis framework is proposed. The fault diagnosis process primarily involves defining the healthy state, decomposing friction curves and their features, and anomaly detection. Given the dependence of friction characteristics on different sources of faults, the parameters of steady-state experimental friction model are divided into two categories: one associated with contact interactions and the other related to non-contact regimes. Subsequently, confidence regions corresponding to distinguishable friction characteristics are independently constructed. These regions encapsulate the statistical description of the healthy state, characterized by mean values and the covariance of the friction characteristic parameter vectors during the unloaded state. In addition, we conduct experiments that consider the influence of applied loads on friction behavior. These experiments serve as a test set for comparison against nominal statistics. Leveraging the similarity between the effects of wear and load on friction, we introduce equivalent load thresholds to assess the severity of joint degradation. The results demonstrate the feasibility of employing confidence region views based on friction characteristic classification for fault detection and isolation.

With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC1 and AC2 of two different fitness functions, Fitness1 and Fitness2, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

In this study, a fuzzy reinforcement learning control (FRLC) is proposed to achieve trajectory tracking of a differential drive mobile robot (DDMR). The proposed FRLC approach designs fuzzy membership functions to fuzzify the relative position and heading between the current position and a prescribed trajectory. Instead of fuzzy inference rules, the relationship between the fuzzy inputs and actuator voltage outputs is built using a reinforcement learning (RL) agent. Herein, the deep deterministic policy gradient (DDPG) methodology consisted of actor and critic neural networks is employed in the RL agent. Simulations are conducted with considering varying slip ratio disturbances, different initial positions, and two different trajectories in the testing environment. In the meantime, a comparison with the classical DDPG model is presented. The results show that the proposed FRLC is capable of successfully tracking different trajectories under varying slip ratio disturbances as well as having performance superiority to the classical DDPG model. Moreover, experimental results validate that the proposed FRLC is also applicable to real mobile robots.

The existing single-mode posture adjustment equipment for solar wing docking is only suitable for a limited number of satellite dimensions; it could not meet the diverse development trends of satellite models. The working range requirements are different when different-sized satellites dock with the solar wing, and the docking process is divided into two stages in this paper. While the DOFs required for the two stages are different, a movable heavy-load reconfigurable redundant posture adjustment platform (RrPAP) with dual motion modes is proposed in this paper. The RrPAP consists of a wheeled mobile platform and a reconfigurable parallel posture adjustment mechanism (PAM). The micro-motion PAM limb types are synthesized, and the comprehensive load-bearing index is proposed to select the mechanism types for heavy-load conditions. A decentralized four-limb six-degree-of-freedom (6-DOF) parallel micro-motion PAM is designed. In the macro-motion stage, for the PAM to still have a defined motion after being released from ground constraints, a serial coupling sub-chain is designed between adjacent limbs to restrict relative movement between them. A type synthesis method for symmetrically coupled mechanisms based on mechanism decoupling and motion distribution is proposed. Four types of symmetrically coupled mechanisms with multi-loop consisting of serial coupling sub-chains are synthesized by using this method. The feasibility of the proposed method is demonstrated through an example using the constraint synthesis method based on screw theory. This work provides a foundation for subsequent refinement and expansion of type synthesis theories and the selection of new types of mechanisms.