## 6 Experiments and Results  
### Simulation of Autonomous Agricultural Vehicles (AAVs)  
The robotic system of autonomous agricultural vehicles (SAAV), based on the KAMAZ vehicle family, with autonomous and remote control, is a human-machine system. The system aims to perform transport tasks using a KAMAZ robotic chassis controlled by a dispatching subsystem. A robotic car is a cyber-physical system; its functioning is based on a combination of complex physical and information processes: the physics of fuel combustion, mechanical motion, material fatigue processes, information processes, intelligent data processing systems, and motion control using machine vision [52]. The construction of full-scale models in the design of such systems is costly, and does not allow for the consideration of various scenarios or prediction of the AAV's performance. In this regard, an effective method is to integrate digital models of AAV aggregates and subsystems and conduct numerous digital tests [53]. In a predictive maintenance system, the parameters characterizing the actual state of the AAV come from the onboard system. These are periodically compared with the AAV models' parameters, taking into account the wear of components and environmental influences. The AAV's state is assessed by the deviation magnitudes of the parameters from the model values. The alert states of the subsystems and units are determined, and the residual life of the equipment and subsystems is forecast. Based on the analysis, the predictive maintenance system generates recommendations for the AAV service personnel regarding the timing of component replacement and the maintenance schedule. SAAV consists of the following subsystems (Figure 8):  
* AAV group;
* Data transfer system;
* AAV system of remote diagnostics and predictive maintenance;
* AAV models (digital twins). task includes five operations. The TASK AV module structure for this case is depicted in Figure 9.  
The production task consists of operations that are simulated by the following transitions:  
* Transition T1 is the AAV's movement along a given route from the home base to the operational position;
* Transitions T2 and T4 are maneuvering near the operating position;
* Transition T3 is the execution of the main production operation;
* Transition T5 is the AAV's return route to the place of deployment. It should be noted that the processes of vehicle maneuvering in the field when approaching the target place, in many cases, take place under increased loads and increased risk of emergencies. The TASK AV module simulates these factors. The HPN AV model structure description provided in the CPN Tools, and the HPN declarations, are shown in Figures A2-A4. An example of evaluating the AAVs' predictive maintenance effectiveness is made for a parallel-sequential schedule of three tasks and three vehicles. It simulates the KAMAZ three models, which correspond to the value of the variable md for color set U:  
\[\text{md}=\text{``M1''}\rightarrow\text{KAMAZ}-43,502-\text{lorry with sides;}\]  
\[\text{md}=\text{``M2''}\rightarrow\text{KAMAZ}-45,143-\text{dump truck;}\]  
\[\text{md}=\text{``M3''}\rightarrow\text{KAMAZ}-65,111-\text{dump truck.}\]  
Figure A3 shows the initial marking of the ZO module, with vehicle models M1 and M2 assigned to BOM1, models M1 and M3 to BOM2, and model M3 to BOM3. Therefore, the initial marking of the ZO module places is represented by multisets:  
\[\text{MZ1}=\text{1''}\text{``M1''}++\text{1''}\text{M2''}\text{M2}=\text{1''} \text{``M1''}++\text{1''}\text{M3''}\text{M23}=\text{1''}\text{M3''}\text{.}\]  
For this example, the final marking of HPN AV is depicted in Figure A4. Studies of KAMAZ family vehicles were carried out in order to assess the degradation and deterioration of their components and assemblies. Tables 6 and 7 list MTBF (mean time between failures)  
Figure 8: Predictive maintenance system for autonomous agricultural vehicles. data for the autonomous vehicles' brake systems and fuel systems.