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 non-trivial 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 results in an efficient representation of large-scale environments, but also offers the scalable and efficient operation of the entire fleet and allows for ex-situ modelling 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 analysed 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 multi-robot/multi-human scenarios such as warehouse logistics, cleaning and maintenance of public spaces.

On Optimising Topology of Agricultural Fields for Efficient Robotic Fleet Deployment

“…Finally, we empirically evaluate our GATO in discrete event simulations with a small farm map and a commercial farm map. Compared with manual topological optimisation in our previous work [4], the proposed autonomous algorithm explores more potential solutions and has the ability to find better solutions or even optimum solutions. This paper further expands the field of autonomous topological optimisation, from optimising of base nodes in our previous work [22] to the proposed method of optimising the cross lanes and drop-off points.…”

“…In this paper, we investigate the autonomous optimisation of a topological map using a Genetic Algorithm (GA), extending our previous work [4] by exploring further topology modification strategies. The GA uses two strategies: 1) adding cross lanes across the farm field; 2) allocating multiple drop-off points (storages) at different regions of the field.…”