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International audience; The development, in the last decades, of technologies for precision agriculture allows the acquisition of crop data with a high spatial resolution. This offers possibilities for innovative control and raises new logistics issues that may be solved using discrete event models. In vineyards, some technologies make it possible to define zones with different qualities of grapes and sort the grapes at harvest to make different vintages. In this context, the Differential Harvest Problem (DHP) consists in finding a trajectory of the harvesting machine in the field in order to obtain at least a given quantity of higher quality grapes and minimising working time. In available literature, the DHP has been solved using Constraint Programming. In this paper, we investigate if it is possible to solve the DHP using the Cost Optimal Reachability Analysis feature of a model-checking tool such as UPPAAL-CORA. A model named DHP_PTA has been designed based on the priced timed automata formalism and the UPPAAL-CORA tool. The method made it possible to obtain the optimal trajectory of a harvesting machine for a vine plot composed of up to 14 rows. The study is based on real vineyard data. This paper is an extended version of a communication presented at WODES 2018 (Saddem-Yagoubi IFAC-PapersOnLine 51(7):57-63, 2018).

International audience; The development, in the last decades, of technologies for precision agriculture allows the acquisition of crop data with a high spatial resolution. This offers possibilities for innovative control and raises new logistics issues that may be solved using discrete event models. In vineyards, some technologies make it possible to define zones with different qualities of grapes and sort the grapes at harvest to make different vintages. In this context, the Differential Harvest Problem (DHP) consists in finding a trajectory of the harvesting machine in the field in order to obtain at least a given quantity of higher quality grapes and minimising working time. In available literature, the DHP has been solved using Constraint Programming. In this paper, we investigate if it is possible to solve the DHP using the Cost Optimal Reachability Analysis feature of a model-checking tool such as UppAal-CORA. A model named DHP PTA has been designed based on the priced timed automata formalism and the UppAal-CORA tool. The method made it possible to obtain the optimal trajectory of a harvesting machine for a vine plot composed of up to 12 rows. The study is based on real vineyard data.

Precision agriculture is a set of methods based on spatial and timed information which aims at making agricultural operations specific to local crop needs. This study was conducted to contribute to precision spraying for trellised perennial crops such as grapevine with a new method called Automata Modelling for Precision Spraying (AMPS). AMPS is based on the timed automata formalism and the UppAal-CORA model-checking tool with cost optimisation. It takes 2D ground-based LiDAR data of the canopy as input and computes a command sequence for the sprayer that is optimized for a cost criterion. AMPS was tested on LiDAR data from a vine row and with a hypothetical sprayer model based on pneumatic technology and individual command for each spout. The chosen cost criterion was the amount of product sprayed on the row. It was demonstrated on the example that a significant proportion of phytosanitary product can be saved. It was also shown that it is necessary to take into account spout control dynamics, because the savings are inferior to the ideal case in which spout response would be instantaneous. [Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE [ADD1_IRSTEA]Équiper l'agriculture International audience

[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]INSPIRE; International audience; Mobile automated systems, such as robots or machinery for precision agriculture, may be designed to perform actions that vary in space according to information from sensors or to a mission map. To be reliable, the design process of such systems should involve the combined verification of spatial and dynamic properties. We consider here CTL model-checking of a mobile robot's behavior, using the UppAal Timed Automata verifier. We consider reachability properties including path finding. Space is modeled as a 2D grid and the mobile robot path is unknown a priori. In this case, the exhaustive state space exploration of model-checking leads to the generation of many possible movements. This exposes such model-checking to combinatorial issues depending on the grid size and the complexity of system dynamics. In this paper, we propose a decomposition methodology reducing the memory requirements for the verification task. The decomposition is twofold. The grid is decomposed in sub-grids and the model-checking query on the whole grid is decomposed in a set of queries on the sub-grids. A set of test cases and check the validity of the decomposition concept. The decomposition methodology is compared to a simpler method that verifies the reachability property without proceeding to decomposition.