ViT-Enabled Task-Driven Autonomous Heuristic Navigation Based on Deep Reinforcement Learning

In unknown environments lacking prior maps, achieving effective visual understanding is crucial for building highly efficient task - driven autonomous navigation systems. In this paper, we propose a vision - enabled goal - oriented autonomous navigation system. This system uses a novel hybrid vision Transformer architecture as the core of its visual perception. Our approach integrates an intermediate waypoint exploration strategy, breaking down a given task into a series of consecutive subtargets. These subtargets are then fed into the scene encoder as an important part of the current physical task state, thereby achieving seamless integration of scene representation and current target information. Based on this, we utilize a deep reinforcement learning framework to develop a local navigation strategy for each subtarget. Given the challenge of addressing the sparse reward function problem, we design a novel hazardous region transfer function.In the simulation experiment stage, we validate the effectiveness of the proposed autonomous navigation system and compare it with other deep - reinforcement - learning - based navigation methods. The experimental results show that our method has significant advantages in terms of navigation success rate and efficiency. Additionally, in the Sim2Real experiments, compared with other algorithms, our method demonstrates greater robustness and mobility.