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 …

Moving in complex environments is an essential capability of intelligent mobile robots.
Decades of research and engineering have been dedicated to developing sophisticated …

In a human-robot coexisting environment, it is pivotal for a mobile service robot to arrive at
the goal position safely and efficiently. In this article, an elastic band-based rapidly exploring …

Inverse reinforcement learning (IRL) attempts to infer human rewards or preferences from
observed behavior. Since human planning systematically deviates from rationality, several …

Existing autonomous robot navigation systems allow robots to move from one point to
another in a collision-free manner. However, when facing new environments, these systems …

Abstract A 1–0 Bg-RRT algorithm is proposed to reduce computational time and complexity,
even in complex environments. Different from Rapidly-exploring Random Tree (RRT) and …

This work presents an approach to learn path planning for robot social navigation by
demonstration. We make use of Fully Convolutional Neural Networks (FCNs) to learn from …

Drones should be allowed to respond to dynamic urban environments and self-adjust their
paths, safely and efficiently. Existing studies fail to develop a comprehensive approach to …

In recent years, learning-based methods have been increasingly studied to combine social
norms with mobile robot navigation, such as social proxemics, distance to pedestrians …

This work presents an approach for learning navigation behaviors for robots using Optimal
Rapidly-exploring Random Trees (RRT^*∗) as the main planner. A new learning algorithm …