Unmanned aerial vehicles (UAVs) have been widely used in many applications due to its small size, swift mobility and low cost. Therefore, the study of guidance, navigation and control (GNC) system of UAV has becoming a popular research direction. Path planning plays an important role in the GNC system. In this paper, a multi-layer path planning algorithm based on reinforcement learning (RL) technique is proposed. Compared to the classic Q-learning, the proposed multi-layer algorithm has a distinct advantage that it collects both global and local information which greatly improves overall performance. The proposed RL algorithm has two layers, the higher layer deals with the local information, which could be considered as a short-term strategy. The lower layer deals with the global information, which could be considered as a long-term strategy. Both the higher layer and lower layer are working in harmony to plan a collision-free path. B-spline curve approach is applied for on-line path smoothing. Simulation results in different scenarios prove the effectiveness of multi-layer Q-learning algorithm.

Calcium carbonate, especially with nanostructure, has been considered as a good candidate material for bone regeneration due to its excellent biodegradability and osteoconductivity. In this study, rod-like calcium carbonate nanoparticles (Rod-CC NPs) with desired water dispersibility were achieved with the regulation of poly (acrylic acid). Characterization results revealed that the Rod-CC NPs had an average length of 240 nm, a width of 90 nm with an average aspect ratio of 2.60 and a negative ζ-potential of −22.25 ± 0.35 mV. The degradation study illustrated the nanoparticles degraded 23% at pH 7.4 and 45% at pH 5.6 in phosphate-buffered saline (PBS) solution within three months. When cultured with MC3T3-E1 cells, the Rod-CC NPs exhibited a positive effect on the proliferation of osteoblast cells. Alkaline phosphatase (ALP) activity assays together with the osteocalcin (OCN) and bone sialoprotein (BSP) expression observations demonstrated the nanoparticles could induce the differentiation of MC3T3-E1 cells. Our study developed well-dispersed rod-like calcium carbonate nanoparticles which have great potential to be used in bone regeneration.

With growing worldwide interests in commercial, scientific, and military issues, there has been a corresponding rapid growth in demand for the development of unmanned aerial vehicles (UAVs) with more reliable and safer motion control abilities. This paper presents a new nonlinear path following scheme integrated with a heading control law for achieving accurate and reliable path following performance. Both backstepping and finite-time techniques are employed for developing the path following and heading control strategies capable of minimizing cross-track errors in finite-time with elegant transient performance, while the barrier Lyapunov function scheme is adopted to limit turning rates of the UAV for preventing it from capsizing which may be induced by overquick steering actions. A fixed-time nonlinear estimator, based on UAV kinematics, is designed for estimating the uncertainties with sideslip angles caused by external disturbances and inertial motions. To avoid the complicated calculation of derivatives of virtual control terms in backstepping, command filters and auxiliary systems are likewise introduced in the design of control laws. Extensive numerical simulation studies on a nonlinear UAV model are conducted to demonstrate the effectiveness of the proposed methodologies.

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