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The paper studies the formed body after completing its autonomous formation task and then proposes a self-healing method that flexibly scales with the scale of the loss in the situation that a large-scale swarm robot is locally missing due to external disturbances. With the stratified mechanism that includes layer-by-layer separation, layer-by-layer filling and iteration loop, the macro-level behavior of a swarm robot is transformed to the local action of an individual robot within the edge layers of the current aggregate. Based on the existing formation technique, driven by the shape genes of the formed body and the changes in the scale of population, the self-healing goal of the large scale of population is transformed into the similar scale of the formed body after scaling. Through the self-healing movement control method centered on individual behavior rules, the bottom-up self-healing configuration is presented on a scaled scale. The paper verifies the feasibility and scalability of this novel method through simulations. The maximum number of robots can reach tens of thousands. Finally, the self-healing method is used to implement the Cilibot robot, a hardware system developed by our laboratory.

In the study of using images to display car paint defects, the current need is to use deep Convolutional Neural Networks (CNN) to identify and classify different types of car paint defects, so as to give full play to the application of image processing in the field of automatic car paint defect detection. Using the collected car paint defect images, the car paint defects dataset is established. The preprocessing process of original data and the application of three image classification models based on CNN are visually displayed. First, the dataset of 7 types of car body defects including bubble, dust, fouling, pinhole, sagging, scratch, and shrink has been established, with a total of 2468 images. The model of MobileNet-V2, Vgg16, and ResNet34 are selected for training. As a result, after 30 training iterations, the MobileNetV2 algorithm achieved 94.3% accuracy, the accuracy of the Vgg16 algorithm is as high as 99.9%, and the accuracy of the ResNet34 algorithm is maintained at 99.2%. To sum up, for car paint defect detection, deep learning has great potential and deserves further development.

Unmanned Aerial Vehicles (UAVs), in multi-UAV collaborative search systems, need to realize adaptive autonomous trajectory planning based on their own state and surrounding environmental conditions. To allow for a fast trajectory planning of multiple UAVs from the current state to the target state, this paper proposes a trajectory planning method for multi-UAV collaborative search tasks based on genetic algorithm (GA). And using genetic algorithm, the UAV's trajectory can be optimized online based on the current mission. Through the analysis of the tasks of the multi-UAV collaborative decision-making part and the autonomous trajectory planning part, the genetic algorithm is used to model and optimize the trajectory planning problem to obtain a feasible and efficient optimal search trajectory from the current state to the decision-making target state. Finally, through specific simulation experiments, the optimal trajectory from the current state to the target state of the UAV i in the multi-UAV system is obtained, which verifies the effectiveness of the algorithm.

Unmanned Aerial Vehicle (UAV) is a kind of aircraft which is pilotless, controlled by remote control or self-control. This paper proposes the design of a multi-UAV cooperative search experimental platform, which uses image detection of searching for targets in image frames to solve the problem of multi-UAV autonomous cooperation and control. This paper will introduce novel solutions to two main problems: multi-UAV autonomous cooperative platform construction and control algorithm verification. The research work is carried out in three parts: hardware and software platform design, multi-UAV autonomous cooperative search algorithm, and platform experimental verification. The experiments include image sensing for searching a target in the image frames, keyboard control, multi-UAV cooperation, and leader-follower formation.

This paper proposes a trajectory tracking control method for wheeled mobile robots with nonholonomic constraints using the combination of the backstepping and direct Lyapunov method. Firstly, we establish the robot’s kinematic model and analyze the trajectory tracking problem. Then, the Sigmoid function is introduced to the backstepping method to improve the convergence rate and global asymptotic stability of the trajectory tracking controller. In addition, the Barbalat Lemma is employed to prove the asymptotic stability of the controller tracking errors. Finally, the co-simulation using V-REP and MATLAB software are presented to validate the performance of our trajectory tracking controller.

The rocker-bogie suspension system has robust capabilities to deal with uneven terrain because of its distributing of the payload over its six wheels uniformly, while there is one major shortcoming to high-speed traversal over the planar terrain. This paper proposes a new dynamic rocker-bogie suspension system with two modes of operation: it can expand the span of the rocker-bogie support polygon to increase travel rate when the terrain is planar; and it can switch to its original configuration to move by low speed when it is faced with rough terrain. The analysis on dynamic stability margin and kinematical simulation on the two operating modes of rocker-bogie are employed to analyze and verify the rationality and effectiveness of the modification in the structure.

This paper presents a combination of variable neighbourhood search and mathematical programming to minimize the sum of earliness and tardiness penalty costs of all operations for just-in-time job-shop scheduling problem (JITJSSP). Unlike classical E/T scheduling problem with each job having its earliness or tardiness penalty cost, each operation in this paper has its earliness and tardiness penalties, which are paid if the operation is completed before or after its due date. Our hybrid algorithm combines (i) a variable neighbourhood search procedure to explore the huge feasible solution spaces efficiently by alternating theswapandinsertionneighbourhood structures and (ii) a mathematical programming model to optimize the completion times of the operations for a given solution in each iteration procedure. Additionally, a threshold accepting mechanism is proposed to diversify the local search of variable neighbourhood search. Computational results on the 72 benchmark instances show that our algorithm can obtain the best known solution for 40 problems, and the best known solutions for 33 problems are updated.

The CXCR4 chemokine receptor is a G protein-coupled receptor that plays an important role in leukocyte homing, cancer metastasis, and human immunodeficiency virus infection. In response to ligand stimulation, chemokine receptors undergo endocytosis through clathrin-coated vesicle (CCV). Uncoating of CCV, a process involving heat shock cognate protein and several other proteins, is critical for fusion of CCV to endosomal compartments. The present study demonstrated that CXCR4 was associated with the 73-kDa heat shock cognate protein (Hsc73) in human embryonic kidney 293 cells in response to ligand stimulation. Truncation of the carboxyl terminal domain of CXCR4 reduced the association with Hsc73 and a glutathione S-transferase-CXCR4 carboxyl terminal fusion protein associated with Hsc73 in vitro, suggesting involvement of the carboxyl terminal domain of the receptor in the interaction. In response to ligand stimulation, CXCR4 underwent internalization and colocalization with Hsc73, but the receptor endocytosis was blocked by knockdown of Hsc73 with RNA interference. Moreover, Hsc73 knockdown significantly reduced the CXCR4-mediated chemotaxis of U87 glioma cell lines. These findings suggest that Hsc73 plays a role in chemokine receptor trafficking and the receptor-mediated chemotaxis.