In this paper, we address the problem of real-time navigation and obstacle avoidance for
automated guided vehicles (AGVs) in dynamic environments, which is a primary research …

In this paper, we propose a soft actor–critic (SAC) algorithm with hindsight experience replay
(HER), called SACHER, which is a class of deep reinforcement learning (DRL) algorithm …

Collision avoidance is a crucial technique to achieve safe and efficient robotic vehicle
navigation in unknown environments. However, moving obstacles with unpredictability in …

View Video Presentation: https://doi. org/10.2514/6.2022-0793. vid Reinforcement learning
(RL) equipped with neural networks has recently led to a wide range of successes in …

Due to random sampling and the unpredictability of moving obstacles, it remains
challenging for mobile robots to effectively learn navigation policies and accomplish …

Mapless navigation for Automated Guided Vehicles (AGV) via Deep Reinforcement
Learning (DRL) algorithms has attracted significantly rising attention in recent years …

In this paper, we propose SACHER (soft actor-critic (SAC) with hindsight experience replay
(HER)), which constitutes a class of deep reinforcement learning (DRL) algorithms. SAC is …

Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …

Abstract Most existing Deep Reinforcement Learning (DRL) algorithms solely apply to
discrete action or continuous action spaces. However, the agent often has both continuous …

In the research of robot systems, path planning and obstacle avoidance are important
research directions, especially in unknown dynamic environments where flexibility and rapid …