UAV Path Planning in Multi-Task Environments with Risks through Natural Language Understanding

Abstract

Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks.