Multi-MAV Autonomous Full Coverage Search in Cluttered Forest Environments

This paper is concerned with autonomous forest full coverage search using multiple micro aerial vehicles (MAVs). Due to the complex and cluttered environment, i.e., many obstacles under the forest canopy, it is quite challenging to achieve full coverage search using fully autonomous MAVs, e.g., quadrotors. In this work, we propose a two-stage multi-MAV forest search strategy. The first batch of MAVs provides a coarse search and mapping result using pre-defined or auto-generated paths. Based on that, the second batch of MAVs continues to search the multiple isolated regions missed by the first batch. The main difficulties fall in the autonomous task allocation and optimal cooperative coverage path planning for the second batch of MAVs, to achieve the full coverage goal. To address this problem, a task allocation algorithm based on the branch and bound principle is introduced to find the optimal search order of the missed regions. Furthermore, an optimal coverage path planning algorithm considering obstacle avoidance is proposed to cover each region. Simulation results show that our proposed method improves the efficiency of coverage path planning for cooperative search and guarantees full area coverage.

Automated summary

This paper focuses on autonomous forest full coverage search using multiple micro aerial vehicles (MAVs). The authors propose a two-stage multi-MAV forest search strategy and solve the difficulties in autonomous task allocation and path planning. Simulation results show that this method improves the efficiency of cooperative search and guarantees full area coverage.