A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

Path planning is an crucial research area in robotics. Compared to other path planning
algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and …

The path planning of a measuring robot is critical to automatic measurement, but it is hard to
solve a global optimal path solution when it comes to scanning a complex body with many …

Autonomous ground vehicles (AGVs) have been deployed in various working environments.
Human-AGV coexisting environments introduce many challenges into the motion planning …

In the process of composite material molding, the role of robots has become increasingly
significant. However, the laying process of composite materials is heavily influenced by …

The difference between training and testing environments is a huge challenge to
generalizing reinforcement learning (RL) algorithms. We propose a soft contrastive learning …

The challenging characteristics of earthwork environments—complex, unstructured, and
constantly evolving—pose significant challenges for the path planning of automated …

Assembly robots have become the core equipment of high-precision flexible automatic
assembly systems with a small working range. Among different fields of robot technology …

Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are
versatile in solving path planning problems. RRT* offers asymptotic optimality but requires …

As a digital environment of interconnected virtual ecosystems driven by measured and
synthesized data, the Metaverse has so far been mostly considered from its gaming …