Aiming at the problems of small good workspace, many singular configurations, and limited carrying capacity of non-redundant parallel mechanisms, a full-redundant drive parallel mechanism is designed and developed, and its performance evaluation, good workspace identification, and scale optimization design are studied. First, the kinematics analysis of the planar 6R parallel mechanism is completed. Then, the motion/force transmission performance evaluation index of the mechanism is established, and the singularity analysis of the mechanism is completed. Based on this, the fully redundant driving mode of the mechanism is determined, and the good transmission workspace of the mechanism in this mode is identified. Then, the mapping relationship between the performance and scale of the mechanism is established by using the space model theory, and the scale optimization of the mechanism is completed. Finally, the robot prototype is made according to the optimal scale, and the performance verification is carried out based on the research of dynamics and control strategy. The results show that the fully redundant actuation parallel mechanism obtained by design optimization has high precision and large bearing capacity. The position repeatability and position accuracy are 0.053 mm and 0.635 mm, respectively, and the load weight ratio can reach 15.83%. The research results of this paper complement and improve the performance evaluation and scale optimization system of redundantly actuated parallel mechanisms.

Differentially flat under-actuated robots are characterized by more degrees of freedom (DOF) than actuators: this makes possible the design of lightweight cheap robots with high dexterity. The main issue of such robots is the control of the passive joint, which requires accurate dynamic modeling of the robot.

Friction is usually discarded to simplify the models, especially in the case of low-speed trajectories. However, this simplification leads to oscillations of the end-effector about the final position, which are incompatible with fast and accurate motions.

This paper focuses on planar $n$-DOF serial robotic arms with $n-1$ actuated rotational joints plus one final passive rotational joint with stiffness and friction properties. These robots, if properly balanced, are differentially flat. When the non-actuated joint can be considered frictionless, differentially flat robots can be controlled in open loop, calculating the motor torques demanded by point-to-point motions. This paper extends the open-loop control to robots with a passive joint with viscous friction adopting a Laplace transform method. This method can be adopted by exploiting the particular structure of the equations of motion of differentially flat under-actuated robots in which the last equations are linear. Analytical expressions of the motor torques are obtained. The work is enriched by an experimental validation of a $2$-DOF under-actuated robot and by numerical simulations of the $2$- and $4$-DOF robots showing the suppression of unwanted oscillations.

In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

Continuum robots offer unique advantages in performing tasks within extremely confined environments due to their exceptional dexterity and adaptability. However, their soft materials and elastic structures inherently introduce nonlinearity and shape instability, especially when the robot encounters external contact forces. To address these challenges, this paper presents a comprehensive model and experimental study to estimate the shape deformation of a switchable rigid-continuum robot (SRC-Bot). The kinematic analysis is first conducted to specify the degrees of freedom (DoF) and basic motions of SRC-Bot, including motion of bending, rotating, and elongating. This analysis assumes that the curvature varies along the central axis and maps the relationship between joint space and driven space. Subsequently, an equivalence concept is proposed to unify the stiffness addressing each DoF, which is then utilized in the establishment of the dynamic model. According to the mechanical structural design, the deformed posture of SRC-Bot is discretized into five segments, corresponding to the distribution of the guiders. The dynamics model is then derived using Newton’s second law and Euler’s method to simulate the deformation under gravity, friction, and external forces. Additionally, the stiffness in three directions is quantified through an identification process to complete the theoretical model. Furthermore, a series of experiments are conducted and compared with simulated results to validate the response and deformed behavior of SRC-Bot. The comparative results demonstrate that the proposed model-based simulation accurately captures the deformable characteristics of the robot, encompassing both static deformed postures and dynamic time-domain responses induced by external and actuation forces.

This paper presents an artificial neural network (ANN)-based nonlinear model predictive visual servoing method for mobile robots. The ANN model is developed for state predictions to mitigate the unknown dynamics and parameter uncertainty issues of the physics-based (PB) model. To enhance both the model generalization and accuracy for control, a two-stage ANN training process is proposed. In a pretraining stage, highly diversified data accommodating broad operating ranges is generated by a PB kinematics model and used to train an ANN model first. In the second stage, the test data collected from the actual system, which is limited in both the diversity and the volume, are employed to further finetune the ANN weights. Path-following experiments are conducted to compare the effects of various ANN models on nonlinear model predictive control and visual servoing performance. The results confirm that the pretraining stage is necessary for improving model generalization. Without pretraining (i.e., model trained only with the test data), the robot fails to follow the entire track. Weight finetuning with the captured data further improves the tracking accuracy by 0.07–0.15 cm on average.

Autonomous fabric manipulation is a challenging task due to complex dynamics and potential self-occlusion during fabric handling. An intuitive method of fabric-folding manipulation first involves obtaining a smooth and unfolded fabric configuration before the folding process begins. However, the combination of quasi-static actions like pick & place and dynamic action like fling proves inadequate in effectively unfolding long-sleeved T-shirts with sleeves mostly tucked inside the garment. To address this limitation, this paper introduces an enhanced quasi-static action called pick & drag, specifically designed to handle this type of fabric configuration. Additionally, an efficient dual-arm manipulation system is designed in this paper, which combines quasi-static (including pick & place and pick & drag) and dynamic fling actions to flexibly manipulate fabrics into unfolded and smooth configurations. Subsequently, once it is confirmed that the fabric is sufficiently unfolded and all fabric keypoints are detected, the keypoint-based heuristic folding algorithm is employed for the fabric-folding process. To address the scarcity of publicly available keypoint detection datasets for real fabric, we gathered images of various fabric configurations and types in real scenes to create a comprehensive keypoint dataset for fabric folding. This dataset aims to enhance the success rate of keypoint detection. Moreover, we evaluate the effectiveness of our proposed system in real-world settings, where it consistently and reliably unfolds and folds various types of fabrics, including challenging situations such as long-sleeved T-shirts with most parts of sleeves tucked inside the garment. Specifically, our method achieves a coverage rate of 0.822 and a success rate of 0.88 for long-sleeved T-shirts folding. Supplemental materials and dataset are available on our project webpage at https://sites.google.com/view/fabricfolding.

Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family.

In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip.

To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

This paper presents a concurrent optimization approach for the design and motion of a quadruped in order to achieve energy-efficient cyclic behaviors. Computational techniques are applied to improve the development of a novel quadruped prototype. The scale of the robot and its actuators are optimized for energy efficiency considering the complete actuator model including friction, torque, and bandwidth limitations. This method and the optimal bounding trajectories are tested on the first (non-optimized) prototype design iteration showing that our formulation produces a trajectory that (i) can be easily replayed on the real robot and (ii) reduces the power consumption w.r.t. hand-tuned motion heuristics. Power consumption is then optimized for several periodic tasks with co-design. Our results include, but are not limited to, a bounding and backflip task. It appears that, for jumping forward, robots with longer thighs perform better, while, for backflips, longer shanks are better suited. To explore the tradeoff between these different designs, a Pareto set is constructed to guide the next iteration of the prototype. On this set, we find a new design, which will be produced in future work, showing an improvement of at least 52% for each separate task.

The cable-driven parallel mechanism (CDPM) is known as an interesting application in industry to pick and place objects owing to its advantages such as large workspaces. In addition to the advantages of this mechanism, there are some challenges to improving performance by considering constraints in different components, such as the behavior of cables, shape, size of the end effector and base, and model of pulleys and actuators. Moreover, the impact of online geometry reconfiguration must be analyzed. This paper demonstrates the impact of these constraints on the performance of reconfigurable CDPM. The methodology is based on the systematic review and meta-analysis guidelines to report the results. The databases used to find the papers are extracted from Scopus and Google Scholar, using related keywords. As a result, the impact of physical constraints on system performance is discussed. A total of 90 and 37 articles are selected, respectively. After removing duplicates and unrelated papers, 88 studies that met the inclusion criteria are selected for review. Even when considering the physical constraints in modeling the mechanism, simplifications in designing a model for the reconfigurable CDPM generate errors. There is a gap in designing high-performance controllers to track desired trajectories while reconfiguring the geometry, and the satisfaction of physical constraints needs to be satisfied. In conclusion, this review presents several constraints in designing a controller to track desired trajectories and improve performance in future work. This paper presents an integrated controller architecture that includes physical constraints and predictive control.

The paper presents a novel control method aimed at enhancing the trajectory tracking accuracy of two-link mechanical systems, particularly nonlinear systems that incorporate uncertainties such as time-varying parameters and external disturbances. Leveraging the Udwadia–Kalaba equation, the algorithm employs the desired system trajectory as a servo constraint. First, the system’s constraints to construct its dynamic equation and apply generalized constraints from the constraint equation to an unconstrained system. Second, we design a robust approximate constraint tracking controller for manipulator control and establish its stability using Lyapunov’s law. Finally, we numerically simulate and experimentally validate the controller on a collaborative platform using model-based design methods.

Dynamic simulations of the cable-driven parallel robots (CDPRs) with cable models closer to reality can predict the motions of moving platforms more accurately than those with idealisations. Hence, the present work proposes an efficient and modular computational framework for this purpose. The primary focus is on the developments required in the context of CDPRs actuated by moving the exit points of cables while the lengths are held constant. Subsequently, the framework is extended to those cases where simultaneous changes in the lengths of cables are employed. Also, the effects due to the inertia, stiffness and damping properties of the cables undergoing 3D motions are included in their dynamic models. The efficient recursive forward dynamics algorithms from the prior works are utilised to minimise the computational effort. Finally, the efficacy of the proposed framework and the need for such an inclusive dynamic model are illustrated by applying it to different application scenarios using the spatial $4$-$4$ CDPR as an example.

Soft robotic devices are designed for applications such as exploration, manipulation, search and rescue, medical surgery, rehabilitation, and assistance. Due to their complex kinematics, various and often hard-to-define degrees of freedom, and nonlinear properties of their material, designing and operating these devices can be quite challenging. Using tools such as optimization methods can improve the efficiency of these devices and help roboticists manufacture the robots they need. In this work, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of soft robots, particularly focusing on literature exploiting evolutionary computation (EC). We completed the search in the IEEE, ACM, Springer, SAGE, Elsevier, MDPI, Scholar, and Scopus databases between 2009 and 2024 using the keywords “soft robot,” “design,” and “optimization.” We categorized our findings in terms of the type of soft robot (i.e., bio-inspired, cable-driven, continuum, fluid-driven, gripper, manipulator, modular), its application (exploration, manipulation, surgery), the optimization metrics (topology, force, locomotion, kinematics, sensors, and energy), and the optimization method (categorized as EC or non-EC methods). After providing a road map of our findings in the state of the art, we offer our observations concerning the implementation of the optimization methods and their advantages. We then conclude our paper with suggestions for future research.

Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

In the pipeline industry, it is often necessary to monitor cracks and damage in pipelines, or need to clean the inside of the pipeline regularly, or collect adhesive on the inner wall of the pipe, but the pipe is too narrow and difficult for humans to enter, it is necessary to use a pipe machine to complete the work. In this paper, a newly designed screw-driven in-pipe inspection robot (IPIR) is proposed. Compared with common robots, this robot innovatively designs adapting mechanism. The robot can not only adapt to the change of the inner diameter size of the pipeline by using the bionic principle and the deformation characteristics of flexible components but also can pass smoothly in the horizontal/oblique/vertical pipelines and has a certain ability to cross obstacles. In addition, it can transmit images of the inner wall of the pipeline wirelessly for data analysis. Finally, through theoretical analysis and prototype construction, the performance of the robot is verified. The results show that the prototype robot can not only smoothly pass through the acrylic pipe with inner diameter of 120–138 mm but also pass through boss with a height of 3 mm.