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Abstract:Although the flowerpot is widely used for indoor flowers, it cannot meet the needs of intelligent management during the uncared-for period. The objective of this study was to design a new microcontroller-based smart flowerpot. Its overall system was composed of three parts: information collection layer, automatic control layer and data transmission layer. Firstly, in the process of collecting information, the Laiyite criterion and the normalized weighted average algorithm were adopted to improve the accuracy of information collection. Secondly, for making precise control decisions, the fuzzy control was used to achieve automatic on-demand watering. Finally, the method for comparative analysis of regional light intensity was utilized to achieve light-seeking and light-supplementing. Experimental results showed that the smart flowerpot had strong anti-jamming performance for information collection, the relative soil moisture of flowers could be stably maintained near the optimum humidity (65%), and the light was well-distributed on the flower with the error angle of light-supplementing ranged from -3° to 3°.

In recent years, the crop protection unmanned aerial vehicle (UAV) has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport. However, there are few researches on obstacle-avoiding path planning for crop protection UAV. In this study, an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints. First, according to the flight parameters of UAV and the types of obstacles in the field, the obstacle circle model and the small obstacle model were established. Second, after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles, the genetic algorithm (GA) was used to search the optimal obstacle-avoiding path. Third, for turning in the path planning, a strategy considering the size of the spray width and the UAV's minimum turning radius was presented, which could decrease the speed change times. The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%, while the path length increased by only 1.6% in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions. With the increase of obstacle radius, the area of overlap and skip reduced effectively with no significant increase in path length. Therefore, the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight.

In this article, aiming at the track prediction to avoid obstacles of the plant-protecting unmanned aerial vehicle, first the track points of the curvature variation of non-uniform distribution according to the characters of the track curvature to avoid obstacles were calculated, and the wind model at the low altitude was built. Then, a tracking prediction method of unmanned aerial vehicle was proposed based on the algorithm of particle filter under the situation of uncertain disturbances. Finally, the track to avoid obstacles was divided into curved track and linear track using the bricks mechanism and the tracking prediction was done. The simulated result shows that proposed method can achieve the tracking prediction better with less smaller and better robustness when there are uncertain Gaussian noise, wind speed below 2 m/s as well as error and random noise of the sensor existing.