Tuning mechanical behaviors of highly entangled hydrogels with the random distribution of mobile entanglements

Highly entangled hydrogels exhibit excellent mechanical properties,including high toughness,high stretchability,and low hysteresis.By considering the evolution of randomly distributed entanglements within the polymer network upon mechanical stretches,we develop a constitutive theory to describe the large stretch behaviors of these hydrogels.In the theory,we utilize a representative volume element(RVE)in the shape of a cube,within which there exists an averaged chain segment along each edge and a mobile entanglement at each corner.By employing an explicit method,we decouple the elasticity of the hydrogels from the sliding motion of their entanglements,and derive the stress-stretch relations for these hydrogels.The present theoretical analysis is in agreement with experiment,and highlights the significant influence of the entanglement distribution within the hydrogels on their elasticity.We also implement the present developed constitutive theory into a commercial finite element software,and the subsequent simulations demonstrate that the exact distribution of entanglements strongly affects the mechanical behaviors of the structures of these hydrogels.Overall,the present theory provides valuable insights into the deformation mechanism of highly entangled hydrogels,and can aid in the design of these hydrogels with enhanced performance.

A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal,Inverse-Kinematic and Geometric-Constraint Error Models

Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically afects the accuracy, reliability, and stability of identifcation results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of diferent error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identifcation accuracy, the impact of noise uncertainty, and parameter identifability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the infuence of the S joint, identifcation with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the diferences among the three error models. The results show that the residual errors of this machine tool are signifcantly improved according to the identifed parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The fndings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators.

A Client Selection Method Based on Loss Function Optimization for Federated Learning

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other users.It trains a globalmodel by aggregating locally-computedmodels of clients rather than their rawdata.However,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global model.For this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local models.We propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global model.It firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in training.Then,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby theNon-IIDdata.Wemake experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

A Stiffness Variable Passive Compliance Device with Reconfigurable Elastic Inner Skeleton and Origami Shell

Devices with variable stiffness are drawing more and more attention with the growing interests of human-robot interaction,wearable robotics,rehabilitation robotics,etc.In this paper,the authors report on the design,analysis and experiments of a stiffness variable passive compliant device whose structure is a combination of a reconfigurable elastic inner skeleton and an origami shell.The main concept of the reconfigurable skeleton is to have two elastic trapezoid four-bar linkages arranged in orthogonal.The stiffness variation generates from the passive deflection of the elastic limbs and is realized by actively switching the arrangement of the leaf springs and the passive joints in a fast,simple and straightforward manner.The kinetostatics and the compliance of the device are analyzed based on an efficient approach to the large deflection problem of the elastic links.A prototype is fabricated to conduct experiments for the assessment of the proposed concept.The results show that the prototype possesses relatively low stiffness under the compliant status and high stiffness under the stiff status with a status switching speed around 80 ms.

Design and Control for WLR-3P:A Hydraulic Wheel-Legged Robot

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex terrain.The hybrid wheel-legged robot(WLR-3P,prototype of the third-generation hydraulic wheel-legged robot)has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough terrains.In this paper,3 design requirements are proposed to improve the mobility and environmental adaptability of the robot.To meet these 3 requirements,2 design principles for each requirement are put forward.First,for light weight and low inertia with high stiffness,3-dimensional printing technology and lightweight material are adopted.Second,the integrated hydraulically driven unit is used for high power density and fast response actuation.Third,the microhydraulic power unit achieves power autonomy,adopting the hoseless design to strengthen the reliability of the hydraulic system.What is more,the control system including hierarchical distributed electrical system and control strategy is presented.The mobility and adaptability of WLR-3P are demonstrated with a series of experiments.Finally,the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.

A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV

In this paper,a bio-inspired path planning algorithm in 3 D space is proposed.The algorithm imitates the basic mechanisms of plant growth,including phototropism,negative geotropism and branching.The algorithm proposed in this paper solves the dynamic obstacle avoidance path planning problem of Unmanned Aerial Vehicle(UAV)in the case of unknown environment maps.Compared with other path planning algorithms,the algorithm has the advantages of fast path planning speed and fewer route points,and can achieve the effect of low delay real-time path planning.The feasibility of the algorithm is verified in the Gazebo simulator based on the Robot Operating System(ROS)platform.Finally,an actual UAV autonomous obstacle avoidance path planning experimental platform is built,and a UAV obstacle avoidance path planning flight test is carried out based on this actual environment.

Prediction of pilot workload in helicopter landing after one engine failure

This paper presents a method to predict the pilot workload in helicopter landing after one engine failure.The landing procedure is simulated numerically via applying nonlinear optimal control method in the form of performance index,path constraints and boundary conditions based on an augmented six-degree-of-freedom rigid-body flight dynamics model,solved by collocation and numerical optimization method.UH-60 A helicopter is taken as the sample for the demonstration of landing after one engine failure.The numerical simulation was conducted to find the trajectory of helicopter and the controls from pilot for landing after one engine failure with different performance index considering the factor of pilot workload.The reasonable performance index and corresponding landing trajectory and controls are obtained by making a comparison with those from the flight test data.Furthermore,the pilot workload is evaluated based on wavelet transform analysis of the pilot control activities.The workloads of pilot control activities for collective control,longitudinal and lateral cyclic controls and pedal control during the helicopter landing after one engine failure are examined and compared with those of flight test.The results show that when the performance index considers the factor of pilot workload properly,the characteristics of amplitudes and constituent frequencies of pilot control inputs in the optimal solution are consistent with those of the pilot control inputs in the flight test.Therefore,the proposed method provides a tool of predicting the pilot workload in helicopter landing after one engine failure.

Efficient learning of robust quadruped bounding usingpretrained neural networks

Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles.The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements.The authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding performance.The NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’approach.The authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.The cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://doi.org/10.1049/csy2.12062.