Path planning is one of the most crucial elements in the field of robotics, such as
autonomous driving, minimally invasive surgery and logistics distribution. This review begins …

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning
has attracted considerable attention and has witnessed substantial development owing to its …

Visual navigation in unknown environments poses significant challenges due to the
presence of many obstacles and low-texture scenes. These factors may cause frequent …

The ability to autonomously navigate safely, especially within dynamic environments, is
paramount for mobile robotics. In recent years, DRL approaches have shown superior …

New robotics applications require robots to complete tasks in social spaces (ie environments
shared with people), thus arising the necessity of enabling robots to operate in a socially …

This work presents the Human Navigation Simulator (HuNavSim), a novel open-source tool
for the simulation of different human-agent navigation behaviors in scenarios with mobile …

Reinforcement Learning-(RL-) based motion planning has recently shown the potential to
outperform traditional approaches from autonomous navigation to robot manipulation. In this …

Path planning is a key process in robotics, playing an important role in fields such as
autonomous driving and logistic delivery. Our work addresses the dual challenges of …

Traditionally, collision-free path planning for industrial robots is realized by sampling-based
algorithms such as RRT (Rapidly-exploring Random Tree), PRM (Probabilistic Roadmap) …

Naturally inspired designs of training environments for reinforcement learning (RL) often
suffer from highly skewed encounter probabilities, with a small subset of experiences being …