As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.

A helicopter is a highly nonlinear system. Its mathematical model is difficult to establish accurately, especially the complicated flapping dynamics. In addition, the forces and moments exerted on the fuselage are very vulnerable to external disturbances like wind gust when flying in the outdoor environment. This paper proposes a composite control scheme which consists of a nonlinear backstepping controller and an extended state observer (ESO) to handle the above problems. The stability of the closed-loop system can be guaranteed based on Lyapunov theory. The external disturbances and model nonlinearities are treated as a lumped disturbance. Meanwhile, the ESO is employed to compensate the influence by estimating the lumped disturbance in real-time. Numerical simulation results are presented to demonstrate that the algorithm can achieve accurate and agile attitude tracking under the external wind gust disturbances even with model uncertainties. When coming to the flight test, a block dropping device was designed to generate a quantifiable and replicable disturbance, and the experimental results indicate that the algorithm introduced above can reject the external disturbance rapidly and track the given attitude command precisely.

Active tracking control is essential for UAVs to perform autonomous operations in GPS-denied environments. In the active tracking task, UAVs take high-dimensional raw images as input and execute motor actions to actively follow the dynamic target. Most research focuses on three-stage methods, which entail perception first, followed by high-level decision-making based on extracted spatial information of the dynamic target, and then UAV movement control, using a low-level dynamic controller. Perception methods based on deep neural networks are powerful but require considerable effort for manual ground truth labeling. Instead, we unify the perception and decision-making stages using a high-level controller and then leverage deep reinforcement learning to learn the mapping from raw images to the high-level action commands in the V-REP-based environment, where simulation data are infinite and inexpensive. This end-to-end method also has the advantages of a small parameter size and reduced effort requirements for parameter turning in the decision-making stage. The high-level controller, which has a novel architecture, explicitly encodes the spatial and temporal features of the dynamic target. Auxiliary segmentation and motion-in-depth losses are introduced to generate denser training signals for the high-level controller’s fast and stable training. The high-level controller and a conventional low-level PID controller constitute our hierarchical active tracking control framework for the UAVs’ active tracking task. Simulation experiments show that our controller trained with several augmentation techniques sufficiently generalizes dynamic targets with random appearances and velocities, and achieves significantly better performance, compared with three-stage methods.

Notopterygium incisum seeds have both morphological and physiological dormancy characteristics and require stratification to break seed dormancy, but the mechanism of seed dormancy release during stratification is still unclear. In this study, different stages of N. incisum seed stratification were employed as experimenta objects, and the dynamic changes during seed dormancy release were studied through embryo morphology, physiological index determination, transcriptome, and metabolome. (1) Stratification treatment reduced the content of stored nutrients in N. incisum seeds, significantly changed enzyme activity, reduced ABA content, and increased GA3 and IAA contents. (2) A total of 110,539 differentially expressed genes (DEGs) and 1656 metabolites (DAMs) were identified during dormancy release. Transcriptome analysis showed that after the dormancy of N. incisum seeds was released, the expression of genes in the abscisic acid signaling pathway (ABI1, PP2CA, ABI5 and ABF4) and the gibberellin signaling pathway (GAI, GAI1 and RGL1) were significantly down-regulated, and there were significant changes in the differentially expressed genes in the auxin, cytokinin and ethylene signaling pathways. The genes related to starch and sucrose metabolism were up-regulated during dormancy release. The genes related to phenylpropanoid and flavonoid biosynthesis were significantly up-regulated after dormancy release. (3) Combined transcriptomics and metabolomics analysis showed that phenylpropanoid biosynthesis and flavonoid biosynthesis are the key pathways for the dormancy release of N. incisum seeds. (4) Metabolomics analysis showed that the accumulation of metabolites of the phenylpropanoid biosynthesis pathway (p-coumaric acid, coniferyl aldehyde, coniferyl glycoside, 5-caffeoylshikimic acid and sinapinic acid) decreased during and after the dormancy release of N. incisum seeds, while the accumulation of flavonoids such as quercetin, rutin, delphinidin and naringenin chalcone increased significantly after dormancy release. Dormancy release in N. incisum seeds involves differential regulation of hormones, carbohydrates, phenylpropanoids, and flavonoid metabolites. Our results provide important insights into the molecular regulatory network of dormancy release in N. incisum seeds.