A Method to Plan the Path of a Robot Utilizing Deep Reinforcement Learning and Multi-Sensory Information Fusion

Nowadays, mobile robots are being widely employed in various settings, including factories, homes, and everyday tasks. Achieving successful implementation of autonomous robot movement largely depends on effective route planning. Therefore, it is not surprising that there is a growing trend in studying and improving the intelligence of this technology. Deep reinforcement learning has shown remarkable performance in decision-making problems and can be effectively utilized to address path planning challenges faced by mobile robots. This manuscript focuses on investigating path planning problems using deep reinforcement learning and multi-sensing information fusion technology. The manuscript elaborates on the significance of path planning, providing comprehensive research encompassing path planning algorithms, deep reinforcement learning, and multi-sensing information fusion. Also, the fundamental theory of deep reinforcement learning is introduced, followed by the design of a multimodal perception module based on image and lidar. A semantic segmentation approach is employed to bridge the gap between simulated and real environments. To enhance strategy, a lightweight multimodal data fusion network model is carefully developed, incorporating modality separation learning. Overall, in this paper, we explore the use of a deep reinforcement learning architecture for conducting path planning experiments with mobile robots. The results obtained from these experiments demonstrate promising outcomes.