A bio-mimetic miniature drone for real-time audio based short-range tracking

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

Blueprints for measuring natural behavior

Until recently laboratory tasks for studying behavior were highly artificial, simplified, and designed without consideration for the environmental or social context. Although such an approach offers good control over behavior, it does not allow for researching either voluntary responses or individual differences. Importantly for neuroscience studies, the activity of the neural circuits involved in producing unnatural, artificial behavior is variable and hard to predict. In addition, different ensembles may be activated depending on the strategy the animal adopts to deal with the spurious problem. Thus, artificial and simplified tasks based on responses, which do not occur spontaneously entail problems with modeling behavioral impairments and underlying brain deficits. To develop valid models of human disorders we need to test spontaneous behaviors consistently engaging well-defined, evolutionarily conserved neuronal circuits. Such research focuses on behavioral patterns relevant for surviving and thriving under varying environmental conditions, which also enable high reproducibility across different testing settings.