A Hybrid Technique for Active SLAM Based on RPPO Model with Transfer Learning

Abstract

The problem of exploration in unknown environments is still a great challenge for autonomous mobile robots due to the lack of a priori knowledge. Active Simultaneous Localization and Mapping (SLAM) is an effective method to realize obstacle avoidance and autonomous navigation. Traditional Active SLAM is usually complex to model and difficult to adapt automatically to new operating areas. This paper presents a novel Active SLAM algorithm based on Deep Reinforcement Learning (DRL). The Relational Proximal Policy Optimization (RPPO) model with deep separable convolution and data batch processing is used to predict the action strategy and generate the action plan through the acquired environment RGB images, so as to realize the autonomous collision free exploration of the environment. Meanwhile, Gmapping is applied to locate and map the environment. Then, based on Transfer Learning, Active SLAM algorithm is applied to complex unknown environments with various dynamic and static obstacles. Finally, we present several experiments to demonstrate the advantages and feasibility of the proposed Active SLAM algorithm.