CACLA-Based Local Path Planner for Drones Navigating Unknown Indoor Corridors

Abstract

This article presents an online local path planning approach for autonomous drone navigating a 2D plane in an unknown, indoor corridor-like environment. The proposed method utilizes a reinforcement learning approach for training a local path planner for navigation in the said environment. With a continuous actor-critic learning automaton (CACLA) applied for continuous action spaces, the proposed algorithm uses a reward structure that formulates a balancing function that gives reward based on balancing the vehicle between artificial potential hills. The drone thereby learns steering control and obstacle avoidance while maintaining a central aligned position with respect to the unknown hallways or corridors. A novel CACLA algorithm and incorporation of a special experience replay memory for the better converging tendency of drone toward the balancing point have been introduced in this article. The proposed reinforcement learning-based online local path planner has been tested on a simulated drone in Gazebo environment.