A Unified Topological Representation for Robotic Fleets in Agricultural Applications

Abstract

Agricultural robots offer a viable solution to the critical challenges of productivity and sustainability of modern agriculture. The widespread deployment of agricultural robotic fleets, however, is still hindered by the overall system's complexity, requiring the integration of several nontrivial components for the operation of each robot but also the orchestration of robots working with each other and human workers. This paper proposes a topological map as the unifying representation and computational model to facilitate the smooth deployment of robotic fleets in agriculture. This topological abstraction of the system state (probabilistic estimates of the functional states and locations of the agents in the environment abstracted to the topological map representations—e.g., human picker at a node $n_p$ and robot traversing on edge $e_{p,q}$) results in an efficient representation of large-scale environments (in many hectares), but also offers the scalable and efficient operation of the entire fleet and allows for ex situ modeling and analysis of operations. The practical use of the proposed framework is demonstrated in a horticultural use case with a fleet of robots supporting the work of human fruit pickers. The critical components of the system are analyzed and evaluated in deployment in both realistic digital twin and real-life soft fruit farms of different scales, demonstrating the scalability and effectiveness of the proposed framework. The presented framework is general and should be easy to adopt in other multirobot/multihuman scenarios, such as warehouse logistics, cleaning, and maintenance of public spaces.