Safe unmanned ground vehicle navigation in unknown rough terrain is crucial for various tasks such as exploration, search and rescue and agriculture. Offline global planning is often not possible when operating in harsh, unknown environments, and therefore, online local planning must be used. In this work, we present a deep reinforcement learning approach for local planning in unknown rough terrain with zero-range to local-range sensing, achieving superior results compared to potential fields or local motion planning search spaces methods. Our approach includes reward shaping which provides a dense reward signal. We incorporate self-attention modules into our deep reinforcement learning architecture in order to increase the explainability of the learnt policy.
