The distributed flexible job shop scheduling problem (DFJSP) has emerged as a critical challenge in the field of scheduling optimization due to its intricate resource allocation and the demand for production–logistics collaboration across multiple factories. However, most existing studies related to DFJSP only focus on the production and transportation process of jobs within a single factory, while neglecting the cross-factory logistics and the heterogeneous characteristics of transportation resources. Therefore, this article first investigates the distributed flexible job shop scheduling problem with heterogeneous transportation (DFJSPHT) resource constraints and proposes an end-to-end deep reinforcement learning (DRL) scheduling method to minimize the makespan. An innovative heterogeneous disjunctive graph model is constructed to uniformly represent the states of factories, machines, operations, and transportation resources in DFJSPHT, and the scheduling process is modeled as a Markov decision process (MDP). Next, a resource release strategy is developed to enhance the efficiency of transportation resources. To enhance the feature expression ability of the model, a graph neural network (GNN) is employed to capture the problem characteristics, and the policy network is trained using the proximal policy optimization. Comparative experiments are conducted on synthetic and benchmark instances demonstrate that the proposed method outperforms the classical priority scheduling rules and two popular DRL-based scheduling methods in solving DFJSPHT, with performance improvements exceeding 10% in most instances.

In intelligent manufacturing systems, accurate and timely fault diagnosis is crucial for ensuring a safe and stable manufacturing process. While transfer learning (TL) can mitigate the need for extensive labeled data, not all historical datasets are applicable to specific fault diagnosis tasks, and the use of inappropriate datasets can deteriorate the accuracy of TL models. To address these issues, a TL fault diagnosis method based on cloud-edge collaboration is proposed. First, a variational autoencoder TL algorithm based on multiscale convolution and domain fusion (MSDF-VAE) is presented to effectively leverage extensive historical fault data, particularly in scenarios with limited labeled samples. Second, a lightweight autoencoder model (LAE) is employed to improve the reusability and specificity of historical data and fault diagnosis models by analyzing the correlation between historical data and the current data. Additionally, to reduce latency and meet real-time requirements for fault diagnosis tasks, a cloud-edge collaborative framework is proposed, within which MSDF-VAE and LAE are deployed. This approach enables real-time diagnosis using the MSDF-VAE model at the edge layer, while the cloud layer concurrently trains a high-precision model with the selected data by the LAE. The experiments verify the accuracy of the MSDF-VAE and confirm the effectiveness of the proposed cloud-edge collaboration framework.

Industrial robots (IRs) have considerable energy-saving potential due to their vast application scale and wide range of applications. Although substantial work on the energy consumption (EC) optimization of IRs has emerged, most optimization approaches require prior knowledge of the IRs' dynamic characteristics and the electro-mechanical parameters of their drive systems, which are typically not provided by IR manufacturers. Therefore, this article proposes an EC modeling and optimization method based on the time-scaling technique and custom identification experimental data without joint torque information. Specifically, this article develops an energy characteristic parameter submodel (ECPSM) to formulate the EC resulting from configuration transitions. In addition, theoretical proof demonstrates that all coefficients in the proposed ECPSM can be identified based on the data of a finite number of identification experiments. Building upon the proposed EC model, a bidirectional dynamic programming (BDP) algorithm optimizes the IR's trajectory for energy-saving, while utilizing parallel processing significantly reduces the time required for the optimization process. Experimental results on the KUKA KR60-3 demonstrate that the proposed method achieves an average relative error of 1.59% for predicting the EC of linear scaling trajectories and 6.19% for nonlinear scaled trajectories. Moreover, the BDP-based optimization method dramatically reduces the computational time required to obtain the optimal scaling trajectory and its EC.

This paper presents a soft actuator inspired by origami and a tunable strain limiting layer, which is proposed for reconfigurable soft robotic grasping. Main structure of the actuator is based on Miura origami which generates extension under pressurized air while a limiting layer with tunable length enables the actuator with different motion patterns. By driving the limiting layer through a servo motor, the range of motion and trajectory of the actuator can be pre-programed and the gripper’s grasping range will be affected accordingly. This paper discusses the design, fabrication, analysis and experimental verification of the actuator. Then grasping performance of the gripper under objects of different shapes, sizes, and weights is experimentally evaluated. The reconfigurable soft gripper can be applied as an end-effector to accomplish adaptive grasping tasks with various targets.

Soft wall-climbing robots have been limited in their ability to perform complex locomotion in diverse environments due to their structure and weight. Thus far, soft wall-climbing robots with integrated functions that can locomote in complex 3-D environments are yet to be developed. This article addresses this challenge by presenting a lightweight (2.57 g) soft wall-climbing robot with integrated linear, turning, and transitioning motion capabilities. The soft robot employs three pneumatic bending actuators and two adaptive electroadhesion pads, which enable it to flip forward, transition between two walls, turn in two directions, and adhere to various surfaces. Different motion and control strategies are proposed based on a theoretical model. The experimental results demonstrate that the robot can move at an average speed of 3.85 mm/s (0.08 body length/s) on horizontal, vertical, and inverted walls and make transitions between walls with different pinch angles within 180°. Additionally, the soft robot can carry a miniature camera on vertical walls to perform detection and surveillance tasks. This article provides a reliable structure and control strategy to enhance the multifunctionality of soft wall-climbing robots and enable their applications in unstructured environments.

Owing to high compliance, adaptiveness, and easy controllability, soft actuators are widely adopted in soft grippers to grasp irregularly shaped or fragile objects. The specific motions can be preprogrammed into the flexible and constrained structures of the actuator, which provides an inexpensive and convenient method for desired motions. However, most preprogrammed structures cannot change the constraints on the actuator to achieve different kinds of deformations, which limits the motion diversities of actuators. This article proposes a scaffold reinforcement mechanism, where rotatable scaffolds distribute on the surface of the soft structure. The orientation adjustments of the scaffolds can change the deformation constraint of the actuator, which results in different kinds of motions. Based on the scaffold reinforcement mechanism, a scaffold-reinforced actuator is proposed, which can achieve bending motion and complex helical motion in the 3-D space by properly adjusting the orientation of the scaffolds. In addition, both the kinematic and mechanical models are proposed to forecast the behavior of the actuator when driven by cable displacement or tension force. Experimental results verify the validity of the theoretical model, and the actuator can achieve an independent control of bending and helical motion, which can be adopted in applications where both high dexterity and flexibility are required.

In the field of sculptured surfaces machining, the robot trajectory planning, under high-order complex constraints, aiming at minimizing energy or time, is always a challenge. The complexity of curvature characteristics of sculptured surfaces and the nonlinearity of relevant constraints are the main reasons. This article proposes an efficient planning method of minimum-energy robot trajectory, for high-speed machining of sculptured surfaces. First, the energy characteristic model of the robot machining system (RMS) is established, to acquire the energy-optimal feedrate, under velocity constraints, to use in the subsequent trajectory planning. Next, a trajectory planning model, with complex constraints, is developed. The proposed method transforms the original trajectory planning into a minimal modification of the initial objective-optimal B-spline feedrate curve (BFC). Furthermore, two main coupling problems, which influence the minimal change of curves, in the direct evolution-based BFC modification, are addressed. Based on the derived solutions, a novel modification algorithm of BFC, with a callback mechanism (CBM), is proposed. Finally, the performance of the proposed method and the specific algorithm is validated by two case studies. Results show that the proposed method can significantly improve the efficiency of robot trajectory planning, aiming at minimum energy, while exhibiting excellent performance.Note to Practitioners—This work was motivated by how to automatically and effectively acquire the energy-saving trajectory of a robotic system, executing sculptured surface machining. This article suggests a new approach that transforms the original trajectory optimization into a minimal modification of the initial energy-optimal BFC, obtained under velocity constraints. This approach calculates the complex energy formula only once. A CBM, for constraint-based BFC modification, is proposed, to revise the redundant reduction of the feedrate. The proposed modification algorithm significantly decreases the detrimental effect of two coupling problems, on the adjustment of the BFC. Results show that the computational efficiency of the proposed method is significantly superior to the one of the direct optimization method. The performance of the proposed modification algorithm is obviously advanced compared to the existing B-spline evolution algorithm. In blade machining, after optimizing the trajectory using the proposed method and algorithm, both energy and time decrease by more than 45% compared to the conservative feed. The proposed algorithm improves significantly the trajectory compared to the prior art algorithm. Technologists benefit from reduced planning time and improved machining performance. However, incorporation into a CAM or manufacturing system has not yet been realized and remains a significant work to be done in the future.