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It was observed that the seedling biomass was correlated with final seed yield in many crops. It is helpful to dissect the genetic architecture of seedling biomass‐related traits for high yield breeding of rapeseed (Brassica napus L.). To date, the genetic architecture of seedling biomass‐related traits is still unclear in B. napus. To identify candidate genes associated with high seedling biomass, a genome‐wide association study of seedling biomass‐related traits under normal growth condition and waterlogging stress was conducted with 156 B. napus accessions. We detected six peaks including 55 single‐nucleotide polymorphisms (SNPs) significantly associated with the above‐mentioned traits in SNP association analysis and 142 UniGenes showed significantly associated with traits in gene expression marker (GEM) association analysis. Candidate genes located within the linkage disequilibrium (LD) blocks were identified, such as ADP GLUCOSE PYROPHOSPHORYLASE LARGE SUBUNIT 1 (BnaC2APL1), PS II OXYGEN‐EVOLVING COMPLEX 1 (BnaC9PSBO1), their ortholog in the Arabidopsis thaliana has been shown to play key roles in starch biosynthesis and photosynthesis. Resequencing of genomic regions revealed the non‐synonymous SNPs in coding regions may be the functional polymorphisms in the two candidate genes. Our results provide insight into the molecular mechanisms underlying the establishment of high‐seedling biomass B. napus and lay a foundation for the characterization of candidate genes aimed at developing high‐seedling B. napus varieties.

In this paper, the pinning control problem of nonlinear multi-robots system (MRS) is investigated. By introducing Linear-Extended-State-Observer (LESO) approach, a LESO based pinning control algorithm is proposed for the multi-robots system with non-identical unknown nonlinear dynamics. By pinning one robot with small coupling strength, a LESO based control algorithm has been developed to guarantee the nonlinear multi-robots system to reach synchronisation when the nonlinear function is given. Furthermore, the tracking errors of the following robots are bounded when the nonlinear function is unknown. Finally, numerical simulations with different scenarios are presented to illustrate the effectiveness of the theoretical results.

Paths will be re-planed timely according the change of obstacles and target position for Human Following Robots (HFRs) so that they can run safely and rapidly. Traditional path planning algorithms are generally focused on how to avoid obstacles or connect with the new target position effectively, however there are rarely studies on the elimination of robot heading fluctuation which is caused by path re-planning. Concerning this issue, we introduce a novel dynamic path re-planning algorithm AT* which is based on A* and walkable area thinning. When robots heading difference value between the current and the last moment which is derived from A* exceeds the threshold, AT* will search for path connection points by walkable area thinning, and then a new path going through optimal connection point and meeting heading constraints will be planned. Thinning algorithm is improved in order to make the passable skeleton can connect path origin and the end. Results from simulation experiments show that AT* algorithm keeps heading stability as a priority at path re-planning and the robot motion stability can be improved effectively.

Additional file 2. The gene model for MeOLE1a. The coding region is marked with uppercase letters, above which are its deduced amino acids (the oleosin domain is shown in red). The start and stop codons are marked with bold letters.

Additional file 6. Kyte���Doolittle hydrophobicity plots of oleosins in physic nut, tung tree, castor bean, M. annua, cassava, and rubber tree.

Additional file 1. The gene model for RcOLE3. The coding region is marked with uppercase letters, above which are its deduced amino acids (the oleosin domain is shown in red). The start and stop codons are marked with bold letters.

Target recognition is a difficult point in intelligently following mobile robots. This paper proposes a multi-target recognition method based on heating markers. The mark is made of carbon fiber material and multiple carbon fibers are arranged in parallel at the waist of the user. Set the order of the carbon fiber energization heating according to the binary rule to realize the function of encoding. The order of the carbon fibers for electric heating is set according to the binary rule to realize the function of encoding. The user can use the portable controller to adjust the temperature of the heating mark. The robot uses an infrared thermal imaging camera to detect the thermal radiation generated by the thermal mark from the front, rear, left, and right directions of the human body. The robot can obtain the position information of the distance heating mark through the ultra-wideband module integrated in the controller. Experiments show that the coded mark can be accurately identified and decoded, and it can meet the requirements of multi-target detection tasks.

In order to reduce radiation hazards, a mobile robot for detecting and grasping radioactive sources is designed. The robot is based on the ROS platform, uses the radioactive source detector to locate the radioactive source and moves to the radioactive source, and uses the mechanical gripper carried by the body to grab the corresponding radioactive source. In order to improve its stability and adaptability, a remote real-time control method is used to realize the motion capture of the detection robot. The robot operates the radioactive source, reducing the environmental pollution of the open-air radioactive source, and at the same time avoiding the radiation damage to the corresponding staff.

In order to achieve precise control of the motion state of a working robot in a nuclear environment, and to ensure that the working robot can work stably in a complex nuclear environment, a neural network PID algorithm is carried out on the driving system of the wheeled mobile robot; in multiple groups of walking units A speed compensator is designed between the control systems to make the control systems of multiple groups of walking units have a coupling relationship, which can ensure the synchronization between multiple groups of walking units. Use the corresponding program code to experiment and simulate the above control method. The experimental results show that the neural network PID can realize the precise control of the robot's motion state. Compared with the traditional PID, it has the characteristics of small overshoot and short transition time; it is realized by the speed compensator established between multiple groups of walking units. The synchronized and coordinated operation of multiple sets of walking units enhances the smoothness of the operation robot and can meet the operation requirements of the operation robot in a nuclear environment.

In this article, we use the Alexnet network in deep learning to determine whether the lung CT images are infected with covid-19.First of all, in the data preprocessing stage, the original CT image data is scaled and normalized to reduce noise interference. The batch operation of training set and test set can increase the training speed;Secondly, build an eight-layer Alexnet network model, set reasonable hyperparameters for each layer of the network, define the loss function and optimizer, and use the processed data to train the weight parameters of each layer in the network model.Finally, three indicators of accuracy, accuracy and recall are used to quantify the effect of model classification, and the impact of different training times on these three indicators is compared to select the best classification model.At the same time, use pyqt5 to write the corresponding auxiliary detection interface to facilitate the display of test results and the selection of classification models.The construction of the network model, the definition of the loss function and the definition of the optimizer are all based on the Pytorch deep learning framework.

In the actual path planning of the mobile robot, the algorithm planning of the robot is almost always to find the shortest path, but if the robot is in a narrow working environment with many corners, it will make the robot too close to the obstacle, and the robot’s edge will contact with the obstacle, resulting in the path planning failure. In order to solve the security problem of mobile robot path planning, we put forward a kind of inflation obstacles, refinement algorithm first determine skeleton and area of the mobile robot on the map, and then the refinement algorithm can get skeleton to improve traffic area, connected to the path of starting point and end point, define the passage area. Then by searching the first and second nodes, we can find the optimal path that accords with the safety of the robot. The new path planning algorithm can realize non-collision planning, improve the safety and stability of the robot, and verify its effectiveness and authenticity through simulation results.

Terrain travel classification is one of the most important content for robot autonomous navigation, especially in terms of following robots. For this reason, a novel travel ability classification method which is suitable for following robots are studied in this paper. First, slope, roughness, and step height of elevation map are used to describe terrain features. Then, robots will acquire these parameters by means of leader track during following behind, and mark these parameters as terrain travel positive samples. By this, the terrain travel ability classification turns into one-class classification only with positive samples, because the robot may not get the full negative samples and it is dangerous for robots to acquire these samples. At last, the SVDD algorithm is used to train terrain classification model, which enables the robot to have the ability of terrain recognition and learning. The experiment results illustrate the feasibility and effectiveness of the proposed algorithms.

A robot is designed for radiation source detection and disposal, which uses a six-wheel suspension mobile platform. The robot carries a radiation source detector to collect the dose rate of the radiation source and locate it. The robot system is composed of a remote control terminal and a mobile robot, which can complete the autonomous positioning and manually searching for the radiation source. The experimental results show that the robot can complete the detection and disposal of the out of control radiation source, and the system runs stably and reliably, ensuring the safety of relevant personnel in the radiation source search process.

In order to improve the sorting efficiency of logistics and reduce the fault tolerance rate, a three-wheel driven omnidirectional reconfigurable conveyor belt is designed in this paper. The conveyor belt we proposed is composed of a number of hexagonal modules, in which each hexagonal module has three omnidirectional wheels installed in different directions. By adjusting the speed of the omni-wheel, it is possible to move goods at a given speed, and adjust the attitude angle of goods in the process of movement so as to arbitrarily change the shipping pose. Furthermore, the algorithm of optimal path planning combining linear transportation and Dijkstra is considered.