Field Evaluation of a Prioritized Path-Planning Algorithm for Heterogeneous Agricultural Tasks of Multi-UGVs

This paper introduces a prioritized path-planning algorithm for heterogeneous tasks performed by multiple unmanned ground vehicles (UGVs) in agricultural environments. The algorithm considers varying robot priorities, thereby extending the traditional multi-agent path finding (MAPF) approach. The proposed algorithm is evaluated in scenarios occurring during representative agricultural operations: harvesting and transportation. An experimental validation is conducted in agriculture-like settings by using multiple simultaneous localization and mapping systems and navigation systems. The results revealed that the path of agent
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, which was assigned the highest priority in both the indoor and outdoor environments, was shortened considerably (3.38 m, 3.6 m, and 5.6 m, respectively). Especially in the face scenario, the sum of changes in distance, calculated using the proposed algorithm was negative, meaning that traffic congestion in the multi-robot system used in the experiment was alleviated without the need for inter-robot communication.