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.

The adaptability of hexapods for various locomotion tasks, especially in rescue and exploration missions, drives their application. Unlike controlled environments, these robots need to navigate ever-changing terrains, where ground irregularities impact foothold positions and origin shifts in contact forces. This dynamic interaction leads to varying hexapod postures, affecting overall system stability. This study introduces a posture control approach that adjusts the hexapod’s main body orientation and height based on terrain topology. The strategy estimates ground slope using limb positions, thereby calculating novel limb trajectories to modify the hexapod’s angular position. Adjusting the hexapod’s height, based on the calculated slope, further enhances main body stability. The proposed methodology is implemented and evaluated on the ATHENA hexapod (All-Terrain Hexapod for Environment Adaptability). Control feasibility is assessed through dynamic analysis of the hexapod’s multibody model on irregular surfaces, using computational simulations in Gazebo software. Environmental complexity’s impact on hexapod stability is tested on both a ramp and uneven terrain. Independent analyses for each scenario evaluate the controller’s effect on roll and pitch angular velocities, as well as height variations. Results demonstrate the strategy’s suitability for both environments, significantly enhancing posture stability.

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.

Collaborative robots are becoming intelligent assistants of human in industrial settings and daily lives. Dynamic model identification is an active topic for collaborative robots because it can provide effective ways to achieve precise control, fast collision detection and smooth lead-through programming. In this research, an improved iterative approach with a comprehensive friction model for dynamic model identification is proposed for collaborative robots when the joint velocity, temperature and load torque effects are considered. Experiments are conducted on the AUBO I5 collaborative robots. Two other existing identification algorithms are adopted to make comparison with the proposed approach. It is verified that the average error of the proposed I-IRLS algorithm is reduced by over 14% than that of the classical IRLS algorithm. The proposed I-IRLS method can be widely used in various application scenarios of collaborative robots.

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.

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.

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.

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.

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.

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.

Challenges/shortages of existing research: Both a topology graph TG and a contracted graph CG are the important tool for the type synthesis of mechanisms and have been studied. Let (b, t, q, p, h) be (binary, ternary, quaternary, pentagonal, hexagonal) link, respectively. Existing TG may include (b, t, q, p, h, …), and existing CG may include (t, q, p, h, …) and exclude b. Therefore, their derivation and isomorphism identification are quite complicated.

The significance of this research: A novel high-contracted topology graph Gj/i including n (q, p, h, …) is proposed in this paper. Since Gj/i excludes (b, t), Gj/i must be more simple than complex TG and CG, and the derivation and isomorphism identification of various Gj/i must be more easily than that of the complex TG and CG.

This paper focuses on the derivation and isomorphism identification of various Gj/i for deriving TG and type synthesis of the complex closed robotic mechanisms with more mechanical advantages and useful functions. First, the conceptions for deriving Gj/i and the relations between Gj/i and the basic links are explained, the relative rules are determined for representing Gj/i and reducing isomorphism of Gj/i using the character strings and the digit groups, and many different Gj/i are represented based on determined rules. Second, many different Gj/i (j = 0, …, 17) are constructed using a circle, several composite curves, and vertex points on the circle based on the character strings and the digit groups. Third, a condition for identifying isomorphism Gj/i is discovered and proved using the composite curves and their connections in Gj/i. Some isomorphism Gj/i are identified from constructed different Gj/i. Finally, different Gj/i (G7/7, G11/11) are illustrated to derive TGs, several complex closed robotic mechanisms are synthesized using derived TGs for creating robotic gripper, a long-stroke robotic pumping unit, and a tunnel robotic excavator.

A decoupling method is proposed for the elastic stiffness modeling of hybrid robots based on the rigidity principle, screw theory, strain energy, and Castigliano’s second theorem. It enables the decoupling of parallel and serial modules, as well as the individual contributions of each elastic component to the mechanism’s stiffness performance. The method is implemented as follows: (1) formulate limb constraint wrenches and corresponding limb stiffness matrix based on the screw theory and strain energy, (2) formulate the overall stiffness matrix of parallel and serial modules corresponding to end of the hybrid robots based on the rigidity principle, principle of virtual work, the wrench transfer formula, and strain energy methods, and (3) obtain and decouple the overall stiffness matrix and deflection of the robot based on the Castigliano’s second theorem. Finally, A planar hybrid structure and the 4SRRR + 6R hybrid robot are used as illustrative examples to implement the proposed method. The results indicate that selectively enhancing the stiffness performance of the mechanism is the most effective approach.

A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.