Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

摘要

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.