Collaborative robotics in manufacturing introduces a new era of seamless human–robot collaboration (HRC), enhancing production line efficiency and adaptability. However, guaranteeing safe interaction while maintaining performance objectives presents significant challenges. Integrating safety with optimal robot performance is paramount to minimize task time and ensure its completion. Our work introduces an architecture for safety in confined human–robot workspaces by integrating existing safety and productivity methods into a unified framework specifically designed for constrained environments. By employing an improved artificial potential field, we optimize paths based on length and bending energy and compare baseline algorithms like gradient descent algorithm and rapidly exploring random tree (RRT*). We propose an evaluation metric for system performance that objectively maps to the system’s safety and efficiency in diverse collaborative scenarios. Additionally, the architecture supports multimodal interaction, including gesture-based inputs, for intuitive control and improved operator experience. Safety measures address static and dynamic obstacles using potential fields and safety zones, with a real-time safety evaluation module adjusting trajectories under specified constraints. A performance recovery algorithm facilitates swift resumption of high-speed operations post safety interventions. Validation includes comparing the algorithmic performance through simulations and experiments using the 6-degrees of freedom UR5 robot by universal robots to identify the most suitable algorithm. Results demonstrate an 83.87% improvement in system performance compared to ideal case scenarios, validating the effectiveness of the proposed architecture, evaluation metric, and multimodal interaction in enhancing safety and productivity.

In recent years, passive motion paradigms (PMPs), derived from the equilibrium point hypothesis and impedance control, have been utilised as manipulation methods for humanoid robots and robotic manipulators. These paradigms are typically achieved by creating a kinematic chain that enables the manipulator to perform goal-directed actions without explicitly solving the inverse kinematics. This approach leverages a kinematic model constructed through the training of artificial neural networks, aligning well with principles of cybernetics and cognitive computation by enabling adaptive and flexible control. Specifically, these networks model the relationship between joint angles and end-effector positions, facilitating the computation of the Jacobian matrix. Although this method does not require an accurate robot model, traditional neural networks often suffer from drawbacks such as overfitting and inefficient training, which can compromise the accuracy of the final PMP model. In this paper, we implement the method using a deep neural network and investigate the impact of activation functions and network depth on the performance of the kinematic model. Additionally, we propose a transfer learning approach to fine-tune the pre-trained model, enabling it to be transferred to other manipulator arms with different kinematic properties. Finally, we implement and evaluate the deep neural network-based PMP on the Universal Robots, comparing it with traditional kinematic controllers and assessing its physical interaction capabilities and accuracy.

Due to the effects of tolerance, design, and manufacturing deviations, there are clearances in the revolute joints of mechanical arms. These clearances can easily lead to system impacts and vibrations, resulting in a decrease in dynamic performance and affecting the trajectory tracking accuracy of the end effector. The existing dynamic models of mechanisms with clearance in revolute joints lack comprehensiveness, universality, and systematicity, and have not addressed the impact of joint reaction forces within clearance revolute joints on the system. The impact collision problem of the revolute joints with clearance was systematically, accurately, and comprehensively modeled and simulated in this study based on multibody dynamics theory. Based on Hertz’s elastic theory, the LuGre friction model, and joint reaction forces, this paper constructs constraint and mechanical models of revolute joints with clearance based on the theory of multibody dynamics. To facilitate multibody dynamics analysis, the collision impact direction matrix is proposed and used for the first time to transform the mechanical model of revolute joints with clearance into external forces. The dynamic models of mobile parallel and double serial manipulators are then constructed. Through numerical simulations on different clearance amounts, tracking trajectories, and load parameters, the impact of revolute joint clearances on system dynamic performance is analyzed. The engineering significance of this research in dynamic analysis of mobile parallel manipulators under imperfect revolute joint conditions is also discussed.

With the rapid advancements in robotics and autonomous driving, SLAM (simultaneous localization and mapping) has become a crucial technology for real-time localization and map creation, seeing widespread application across various domains. However, SLAM’s performance in dynamic environments is often compromised due to the presence of moving objects, which can introduce errors and inconsistencies in localization and mapping. To overcome these challenges, this paper presents a visual SLAM system that employs dynamic feature point rejection. The system leverages a lightweight YOLOv7 model for detecting dynamic objects and performing semantic segmentation. Additionally, it incorporates optical flow tracking and multiview geometry techniques to identify and eliminate dynamic feature points. This approach effectively mitigates the impact of dynamic objects on the SLAM process, while maintaining the integrity of static feature points, ultimately enhancing the system’s robustness and accuracy in dynamic environments. Finally, we evaluate our method on the TUM RGB-D dataset and in real-world scenarios. The experimental results demonstrate that our approach significantly reduces both the root mean square error (RMSE) and standard deviation (Std) compared to the ORB-SLAM2 algorithm.

Humanoid robots are highly redundant, and finding whole-body optimal trajectories for various tasks is very complex. This paper proposes a method to find an energy-optimal, dynamically balanced, and collision-free trajectory of the 20 degrees of freedom humanoid robot in pick and place application. The task of pick and place is divided into three subtasks using the Pseudoinverse Jacobian method of redundancy resolution. The three subtasks are end effector trajectory represented by $\mathcal {T}_1$, hip trajectory represented by $\mathcal {T}_2$, and maximizing the manipulability represented by $\mathcal {T}_3$. The Pseudoinverse Jacobian method is coupled with particle swarm optimization (PSO) to find the optimal trajectories. The main contribution of this paper is the decomposition of the whole-body task of the humanoid robot into three distinct subtasks to find energy-optimal, dynamically balanced, and obstacle-free trajectories. The concept of virtual surface is used to avoid dragging objects on the table surface. The problem is optimized with Particle Swarm Optimization. Simulations were conducted to pick up and place objects from a table and constrained spaces like a drawer. The results show that the robot can pick and place objects from defined locations on the table.

The estimation of workspace for parallel kinematic machines (PKMs) typically relies on geometric considerations, which is suitable for PKMs operating under light load conditions. However, when subjected to heavy load, PKMs may experience significant deformation in certain postures, potentially compromising their stiffness. Additionally, heavy load conditions can impact motor loading performance, leading to inadequate motor loading in specific postures. Consequently, in addition to geometric constraints, the workspace of PKMs under heavy load is also constrained by mechanism deformation and motor loading performance.

This paper aims at developing a new heavy load 6-PSS PKM for multi-degree of freedom forming process. Additionally, it proposes a new method for estimating the workspace, which takes into account both mechanism deformation and motor loading performance. Initially, the geometric workspace of the machine is predicted based on its geometric configuration. Subsequently, the workspace is predicted while considering the effects of mechanism deformation and motor loading performance separately. Finally, the workspace is synthesized by simultaneously accounting for both mechanism deformation and motor loading performance, and a new index of workspace utilization rate is proposed. The results indicate that the synthesized workspace of the machine diminishes as the load magnitude and load arm increase. Specifically, under a heavy load magnitude of 6000 kN and a load arm of 200 mm, the utilization rate of the synthesized workspace is only 9.9% of the geometric workspace.

A transverse ledge climbing robot inspired by athletic locomotion is a customized robot aiming to travel through horizontal ledges in vertical walls. Due to the safety issue and complex configurations in graspable ledges such as horizontal, inclined ledges, and gaps between ledges, existing well-known vision-based navigation methods suffering from occlusion problems may not be applicable to this special kind of application. This study develops a force feedback-based motion planning strategy for the robot to explore and make feasible grasping actions as it continuously travels through reachable ledges. A contact force detection algorithm developed using a momentum observer approach is implemented to estimate the contact force between the robot’s exploring hand and the ledge. Then, to minimize the detection errors due to dynamic model uncertainties and noises, a time-varying threshold is integrated. When the estimated contact force exceeds the threshold value, the robot control system feeds the estimated force into the admittance controller to revise the joint motion trajectories for a smooth transition. To handle the scenario of gaps between ledges, several ledge-searching algorithms are developed to allow the robot to grasp the next target ledge and safely overcome the gap transition. The effectiveness of the proposed motion planning and searching strategy has been justified by simulation, where the four-link transverse climbing robot successfully navigates through a set of obstacle scenarios modeled to approximate the actual environment. The performance of the developed grasping ledge searching methods for various obstacle characteristics has been evaluated.

The human hand’s exceptional dexterity and compliance, derived from its rigid-soft coupling structure and tendon-driven interphalangeal coordination, inspire robotic grippers capable of versatile grasping and force adaptation. Traditional rigid manipulators lack compliance for delicate tasks, while soft robots often suffer from instability and low load capacity. To bridge this gap, we propose a biomimetic multi-joint composite finger integrating a 3D-printed rigid phalanges (46–51 mm) with dual fabric-reinforced pneumatic bladders, mimicking human finger biomechanics. This hybrid design combines hinge-jointed rigidity and anisotropic fabric constraints, enabling two rotational degrees of freedom with higher radial stiffness, achieving 2.18× higher critical burst pressure (240 kPa) than non-reinforced bladders, while preserving axial compliance. Experimental validation demonstrates a 4.77 N maximum fingertip force at 200 kPa and rapid recovery (< 2s) post-impact. The composite finger exhibits human-like gestures (enveloping, pinching, flipping) and adapts to irregular/fragile objects (e.g., eggs, screws) through coordinated bladder actuation. Assembled into a modular gripper, it sustains 1 kg payloads and executes thin-object flipping via proximal-distal joint synergy. This rigid-soft coupling design bridges compliance and robustness, offering high environmental adaptability for applications in industrial automation, human–robot interaction, and delicate manipulation.

Optical microrobots are activated by a laser in a liquid medium using optical tweezers. To create visual control loops for robotic automation, this work describes a deep learning-based method for orientation estimation of optical microrobots, focusing on detecting 3-D rotational movements and localizing microrobots and trapping points (TPs). We integrated and fine-tuned You Only Look Once (YOLOv7) and Deep Simple Online Real-time Tracking (DeepSORT) algorithms, improving microrobot and TP detection accuracy by $\sim 3$% and $\sim 11$%, respectively, at the 0.95 Intersection over Union (IoU) threshold in our test set. Additionally, it increased mean average precision (mAP) by 3% at the 0.5:0.95 IoU threshold during training. Our results showed a 99% success rate in trapping events with no false-positive detection. We introduced a model that employs EfficientNet as a feature extractor combined with custom convolutional neural networks (CNNs) and feature fusion layers. To demonstrate its generalization ability, we evaluated the model on an independent in-house dataset comprising 4,757 image frames, where microrobots executed simultaneous rotations across all three axes. Our method provided mean rotation angle errors of $1.871^\circ$, $2.308^\circ$, and $2.808^\circ$ for X (yaw), Y (roll), and Z (pitch) axes, respectively. Compared to pre-trained models, our model provided the lowest error in the Y and Z axes while offering competitive results for X-axis. Finally, we demonstrated the explainability and transparency of the model’s decision-making process. Our work contributes to the field of microrobotics by providing an efficient 3-axis orientation estimation pipeline, with a clear focus on automation.

The mandible is crucial for human physiological functions, as well as facial esthetics and expressions. The mandibular reconstruction surgery has dual challenges of restoration of both facial form and physiological function, which demands high precision in positioning and orientation of the bone graft. The traditional manual surgery heavily relies on surgeon’s experience. Although the computer image-guided surgery improves the positioning accuracy, the manual manipulation is still difficult to achieve precise spatial orientation of objects, resulting in unsatisfactory intraoperative execution of preoperative surgical design. This paper integrates computer image navigation and robotic technology to assist mandible reconstruction surgery, which empowers surgeons to achieve precise spatial localization and orientation adjustment of bone grafts. The kinematic analysis is conducted, and an improved Iterative Closest Point (ICP) algorithm is proposed for spatial registration. A novel hand-eye calibration method for multi-arm robot and spatial registration of free bone blocks are proposed. The precision experiment of the image-guided navigation and the animal experiments are carried out. The impact of registration point numbers on spatial registration accuracy is analyzed. The results show the feasibility of the robot-assisted navigation for mandibular reconstruction surgery. The robotic system can improve the orientation accuracy of bone blocks to enhance the effectiveness of surgery.

Variable topological space robots are essential for providing adaptability and flexibility, enabling the robot to adjust its morphology to perform a range of tasks in the unstructured environment of space. However, impact is a common consequence of topology transformation in space robotics, which may lead to irreversible damage, such as the shedding of solid lubrication on joints. Nevertheless, determining the precise force-time relationships of such impacts poses significant challenges, especially when accounting for various connection mechanisms. In this work, a docking strategy that optimizes the manipulator’s joint angle configuration to minimize the impulse when the topology changes is proposed. First, an estimation technique is developed to quantify the impulse generated by topology transformation, employing spatial operator algebra and generalized momentum balance equations. Based on this model, the impulse minimization is modelled as a bilevel optimization problem, which decomposes a complex multipolar problem into two simpler subproblems. Although this optimization model may compromise computational efficiency, it increases the probability of achieving an optimal solution. To address this, a bilevel solution strategy based on a heuristic algorithm is proposed. In this framework, the lower level uses particle swarm optimization to determine the global optimum, while the upper level adopts simulated annealing to enhance computational speed. Finally, simulations are conducted to validate the proposed approach. Results demonstrate that the proposed method substantially reduces impulse.

Remote center-of-motion (RCM) manipulators are a key issue in minimally invasive surgeries (MIS). The existing RCM parallel mechanisms (PMs) can only generate RCM motion based on the invariant RCM. To provide mobility for RCM, this paper designed a new family of RCM PMs with movable RCM that features a double-stage topological structure. Drawing mainly on configuration evolution and Lie-group, a general approach is proposed to design double-stage PMs with movable RCM. Feasible limbs for 2R1T RCM motion are enumerated and used to construct the secondary PM. Type synthesis of the primary PMs that realize movable RCM is accomplished based on the method presented. Different connection styles between the two stages that ensure the geometrical conditions of RCM motion are designed. Using different connection styles, double-stage PMs with movable RCM are constructed. These new RCM PMs can realize precise positioning of RCM by taking advantage of the primary PMs, which indicates their potential application prospects in MIS.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

Stroke is a prevalent neurological event that often induces significant motor impairments in the upper extremities, such as hemiplegia, which impacts bimanual coordination and fine motor skills. Robotic-assisted therapy has gained prominence as a contemporary rehabilitation modality, providing augmented motor repetitions and proprioceptive feedback, thereby potentiating neuroplasticity and functional recovery. This pilot study aimed to examine the therapeutic efficacy of a robotic intervention for wrist rehabilitation in two post-stroke adults aged 50–70 years. The intervention protocol, implemented biweekly over four weeks, encompassed 45-minute sessions consisting of passive muscle elongation (5 min) and robotic-facilitated exercises targeting pronation-supination (10 min), flexion-extension (10 min), and radial-ulnar deviation (10 min). Outcome measures included pre- and post-intervention assessments utilizing the motor activity log, Fugl-Meyer Scale, and robotic metrics for muscular strength. Results indicated enhancements in joint range of motion, motor precision, and neuromuscular control, with patient “B” demonstrating superior improvements, particularly in complex motor patterns. In contrast, patient “A” exhibited attenuated progress, attributable to pronounced baseline deficits and fatigue. Specific gains were observed in flexion-extension for patient “A” and pronation-supination for patient “B,” with minimal advancements in radial-ulnar deviation across both subjects. These findings provide preliminary evidence supporting the efficacy of robotic-assisted therapy in motor rehabilitation post-stroke with the novel proposed wrist rehabilitation device.

Reduced-order models encapsulating complex whole-body dynamics have facilitated stable walking in various bipedal robots. These models have enabled intermittent control methods by applying control inputs intermittently (alternating between zero input and feedback input), allowing robots to follow natural dynamics and provide energetically and computationally efficient walking. However, due to their inability to derive closed-form solutions for the angular momentum generated by swing motions and other dynamic actions, constructing a precise model for the walking phase with zero input is challenging, and controlling walking behavior using an intermittent controller remains problematic. This paper proposes an intermittent controller for bipedal robots, modeled as a multi-mass system consisting of an inverted pendulum and an additional mass representing the swing leg. The proposed controller alternates between feedback control during the double support (DS) phase and zero-input control during the single support (SS) phase. By deriving a constrained trajectory, the system behaves as a conservative system during the SS phase, enabling closed-form solutions to the equations of motion. This constraint allows the robot to track the target behavior accurately, intermittently adjusting energy during the DS phase. The effectiveness of the proposed method is validated through simulations and experiments with a bipedal robot, demonstrating its capability to accurately and stably track the target walking velocity using intermittent control.

The safety of human-collaborative operations with robots depends on monitoring the external torque of the robot, in which there are toque sensor-based and torque sensor-free methods. Economically, the classic method for estimating joint external torque is the first-order momentum observer (MOB) based on a physic model without torque sensors. However, uncertainties in the dynamic model, which encompasses parameters identification error and joint friction, affect the torque estimation accuracy. To address this issue, this paper proposes using the backpropagation neural network (BPNN) method to estimate joint external torque without the delicate physical model by utilizing the powerful machine learning ability to handle the uncertainties of the MOB method and improve the accuracy of torque estimation. Using data obtained from the torque sensor to train the BPNN to build up a digital torque model, the trained BPNN can perceive force in practical applications without relying on the torque sensor. In the end, by contrast to the classic first-order MOB, the result demonstrates that BPNN achieves higher estimation accuracy compared to the MOB.

The enhanced computing power of the onboard flight control system and the low flapping frequency have made real-time position and attitude control possible for large flapping-wing flying robots (LFWFRs). Therefore, it is necessary to design an efficient flapping load calculation method to provide the load situation of the flapping wings. To address this problem, we establish a three-dimensional aeroelastic model by coupling the finite element method and state-space airloads theory. This model considers the interaction between aerodynamic loads, inertial loads, and flapping-wing structural elasticity during the flapping motion, which could quickly calculate the instantaneous aerodynamic loads and inertial loads of flapping wings under different flight conditions. The accuracy of the model was verified through vacuum and wind tunnel experiments. Experiments under various flight conditions demonstrate the effectiveness and reliability of the proposed method, and the method could be used to guide the rapid iterative upgrade and control law design of LFWFRs.

This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.

This article presents a comprehensive evaluation of two nuclear-rated bilateral telerobotic systems, Telbot and Dexter, focusing on critical performance metrics such as effort transparency, stiffness, and backdrivability. Despite the absence of standardized evaluation methodologies for these systems, this study identifies key gaps by experimentally assessing the quantitative performance of both systems under controlled conditions. The results reveal that Telbot exhibits higher stiffness, but at the cost of greater effort transmission, whereas Dexter offers smoother backdrivability. Furthermore, positional discrepancies were observed during the tests, particularly in nonlinear positional displacements. These findings highlight the need for standardized evaluation methods, contributing to the development, manufacturing, and procurement processes of future bilateral telerobotic systems.

Cable-driven parallel robots (CDPRs) have been widely used as motion executers for their large workspace and lower inertia. However, there are few studies on structural optimization design considering its stability. This paper proposes a stability optimization method based on force-position workspace for a reconfigurable cable-driven parallel robot (RCDPR). First, the structural optimization analysis of RCDPR is carried out. Then, the forces distribution algorithm based on the feasibility of real-time control is determined, and the boundary contour algorithm (BCA) of the RCDPR force feasible workspace (FFW) on the central plane is proposed. Second, the stiffness and cables driving force space (CFS) models of RCDPR are established. Subsequently, the stability evaluation function is established to optimize the structure of RCDPR, which uses FFW and main task feasible workspace (MFW) as carriers and stiffness and CFS as weights. Finally, an experimental prototype of the developed robot is constructed, and motion performance and workspace verification experiments are conducted. The results demonstrate that the developed RCDPR has good motion accuracy and stable workspace, and the results also verify the feasibility of the stability evaluation function and BCA.