Multi-sensor based strategy learning with deep reinforcement learning for unmanned ground vehicle

Abstract

As intelligent Unmanned Ground Vehicles (UGVs) find broader applications in areas such as transportation and logistics. The fusion of multiple sensors becomes crucial, since it not only amplifies UGV perception in dynamic scenarios but also underpins their autonomous decision-making capabilities. However, many existing methods only focus on single-sensor data, overlooking the multi-sensor data integration, thereby limiting UGV's scalability and adaptability. In this paper, we introduce the Multi-Sensor Collaborative Decision Network (MSCDN) for autonomous multi-sensor fusion policy learning designed specifically for UGVs. MSCDN is dedicated to integrate the data collected by multi-sensors in simulation environment and can be migrate to real environment. Firstly, a simulation environment mirroring real environment is created, using a framework that transfers UGV decision-making from simulated to real environment with deep reinforcement learning. Secondly, MSCDN uses a multi-sensor attention fusion network to adaptively integrate sensor data, refining UGV responses in dynamic settings. Thirdly, MSCDN's efficacy is tested on both simulated and real UGV lane-keeping tasks, showcasing its superior performance in comparative experiments. Compared to baseline methods, MSCDN reduces training steps and achieves a 35.71 % higher success rate and a 37.5 % quicker task completion time, underlining its proficient multi-sensor data fusion capability.

Keywords

• Multi-sensor
• Data fusion
• Deep reinforcement learning
• Unmanned ground vehicle
• Multi-source attention mechanisms