The path planning issue of the underactuated autonomous underwater vehicle (AUV) under
ocean current disturbance is studied in this paper. In order to improve the AUV's path …

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …

Conventional navigation techniques have mainly relied on a global information approach,
wherein pre-built laser or camera environment maps are used to construct a path from a …

Purpose Compared with traditional methods relying on manual teaching or system
modeling, data-driven learning methods, such as deep reinforcement learning and imitation …

The Internet of Things and the millions of devices that generate and collect data through
sensors to send it to the Cloud are part of the life of users in many contexts, including smart …

This paper proposes iTD3-CLN, a Deep Reinforcement Learning (DRL) based low-level
motion controller, to achieve map-less autonomous navigation in dynamic scene. We …

This paper presents an end-to-end online learning navigation method based on deep
reinforcement learning (DRL) for mobile robots, whose objective is that mobile robots can …

In this article, we propose a deep reinforcement learning (DRL) algorithm as well as a novel
tailored neural network architecture for mobile robots to learn navigation policies …

Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation.
In this paper, we propose a novel deep neural network (DNN) based method for real-time …

It is an essential capability of indoor mobile robots to avoid various kinds of obstacles.
Recently, multimodal deep reinforcement learning (DRL) methods have demonstrated great …