Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

The key module for autonomous mobile robots is path planning and obstacle avoidance.
Global path planning based on known maps has been effectively achieved. Local path …

In some specific conditions, UAVs are required to obtain comprehensive information of an
area or to operate in the area in an all-round way. In this case, the coverage path planning …

The path planning problem of mobile robot in unknown dynamic environment (UDE) is
discussed in this article by building a continuous dynamic simulation environment. To …

The monitoring of water resources using Autonomous Surface Vehicles with water-quality
sensors has been a recent approach due to the advances in unmanned transportation …

A robot path planning algorithm based on reinforcement learning is proposed. The algorithm
discretizes the information of obstacles around the mobile robot and the direction …

Shape-shifting robots are the feasible solutions to solve the Complete Coverage Planning
(CCP) problem. These robots can extend the covered areas by reconfiguring their shape to …

Coverage path planning (CPP) is a critical problem in robotics, where the goal is to find an
efficient path that covers every point in an area of interest. This work addresses the power …

Investigating the path planning of multiple unmanned aerial vehicles (UAVs) covering
multiple regions, this work proposes an effective heuristic method of region coverage path …