7 accepted papers
Quadrotors performing aerial tasks are vulnerable to sudden external disturbances, which may lead to instability, control loss, or even structural damage such as broken arms or frame failure. These threats are particularly critical during flight, where recovery opportunities are limited. To address
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without alterin
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Conventional DRL approaches predominantly learn forward-motion policies, causing robots to become trapped in complex enviro
This paper proposes a whole process method of multi-quadrotors from detecting and locating to encircle and track targets. The reconnaissance quadrotor realizes accurate target detection based on the one-stage target detector of convolutional neural network. Then, based on a pinhole camera projection
Agile flight is significant for target tracking, search and rescue, and delivery applications. To achieve agile flight, we can exploit the actuator’s potential by utilizing the full dynamics of the quadrotor. However, the 6-degrees-of-freedom dynamics render the optimization problem non-convex, and…
Stock Trend Prediction(STP) has drawn wide attention from various fields, especially Artificial Intelligence. Most previous studies are single-scale oriented which results in information loss from a multi-scale perspective. In fact, multi-scale behavior is vital for making intelligent investment dec…