LATEST RESEARCH
In this work, we propose a deep reinforcement learning algorithm as well as a novel tailored neural network architecture for mobile robots to learn navigation policies autonomously. To the best of our knowledge, this work is the first study that has developed a DRL model which is capable to achieve depth-based autonomous navigation in an end-to-end manner while also outperforming the conventional method. Specifically, we introduce a new feature extractor to better acquire critical spatiotemporal features from raw depth images. The obtained features are combined with the encoded destination information and mapped to control commands directly. ...
Real-time path planning is crucial for robots to achieve autonomous navigation. Therefore, in this work, we propose a novel deep neural network (DNN) based method for real-time online path planning in completely unknown cluttered environments. Firstly, an end-to-end DNN architecture named online three-dimensional path planning network (OTDPP-Net) is designed to learn 3D local path planning policies. It determines actions in 3D space based on multiple value iteration computations approximated by recurrent 2D convolutional neural networks. Furthermore, a path planning framework is also developed accordingly to realize real-time online path plan...
It is vital for mobile robots to achieve safe autonomous steering in various changing environments. In this work, a novel end-to-end network architecture is proposed for mobile robots to learn steering autonomously through deep reinforcement learning. Specifically, two sets of feature representations are firstly extracted from the depth inputs through two different input streams. The acquired features are then merged together to derive both linear and angular actions simultaneously. Moreover, a new action selection strategy is also introduced to achieve motion filtering by taking the consistency in angular velocity into account. Besides, in a...
It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-end deep reinforcement learning algorithm in this work to improve the performance of autonomous steering in complex environments. By embedding a branching noisy dueling architecture, the proposed model is capable of deriving steering commands directly from raw depth images with high efficiency....