Autonomous Exploration Method for Fast Unknown Environment Mapping by Using UAV Equipped with Limited FOV Sensor

TL;DR

This paper introduces a novel autonomous exploration planner for UAVs that enhances efficiency in unknown environments by optimizing exploration paths, yaw planning, and dynamic replanning, validated through simulation and real-world experiments.

Contribution

It proposes a new exploration sequence generation method considering ATSP, an adaptive yaw planning strategy, and a dynamic replanning approach to improve exploration efficiency.

Findings

Better flight time and distance performance compared to existing methods

Effective in both simulation and real-world environments

Enhanced coverage of frontiers through adaptive yaw planning

Abstract

Autonomous exploration is one of the important parts to achieve the fast autonomous mapping and target search. However, most of the existing methods are facing low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner (FAEP) is proposed in this paper. Different from existing methods, we firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors in the asymmetric traveling salesman problem (ATSP). Then, according to the exploration sequence and the distribution of frontiers, an adaptive yaw planning method is proposed to cover more frontiers by yaw change during an exploration journey. In addition, to increase the speed and fluency of flight, a dynamic replanning strategy is also adopted. We present sufficient comparison and evaluation experiments in simulation environments. Experimental results show the proposed exploration planner has better performance in terms of flight time and flight distance compared to typical and state-of-the-art methods. Moreover, the effectiveness of the proposed method is further evaluated in real-world environments.