Multi-Agent target allocation and safe trajectory planning for artificial pollination tasks

Abstract

This paper presents an autonomous multi-agent drone system utilized for an artificial pollination task to address the declining bee population and its impact on natural pollination. The main goal is to visit all the targets (flowers) by drones. The system receives the position of the targets located on a vertical fruiting wall from a single sensor mounted on an unmanned ground vehicle (UGV). Then, it determines the sequence of target allocation to minimize the task time and to minimize the accumulated distance each drone travels. The limited payload capacity of each drone and the need to refill stock carried by the UGV are also considered. After allocating the targets, a safe trajectory is generated for each drone to avoid collision with other drones, considering uncertainty in the drones’ positions. When a drone reaches a target, the system allocates a new target, and the cycle repeats until the task is completed. The system is reactive to the evolution of the task and adjusts accordingly. An additional tested factor is the impact of the positioning error on the task time. The system was evaluated through dynamic simulations using real datasets of peach and pear orchards and finally in a laboratory proof-of-concept experiment using a micro-drone system.