Abstract

The chapter deals with the mathematical model for planning the optimal movement route, which has been implemented in the Tactical Decision Support System (TDSS). The model processes and evaluates the data contained in the five raster layers, which are tactically relevant for planning the movement route of troops or autonomous vehicles on the battlefield. The basis for calculating the optimal movement route is a ground surface layer, which is then modified by algorithmic and criterion relationships with the layers of hypsometry, weather attack, and the activities of enemy and friendly units. The result of mathematical model calculations is a time-optimized and safe movement route displayed on the topographic basis. The experiments realized have verified the function of the optimal movement route model when neither the reconnaissance group nor the autonomous vehicle was observed by the enemy. The total time of the UGV with the use of the TDSS to cover the route of maneuver was 67 minutes shorter than the real time of the BRAVO group movement with the use of the TDSS and 105 minutes shorter than the real time of the ALFA group without the use of the TDSS. The comparison of responses to the attack shows that the BRAVO group using the Maneuver Control System (MCS CZ) as part of the TDSS has destroyed the attackers faster by 71 seconds than the ALFA group without the use of the TDSS.

Highlights

  • Over the past 10 years, a considerable progress has been observed in the field of autonomous systems in various fields of activity

  • The same method of maneuver and approach was used in the experiment with a ground autonomous vehicle, the execution of which was calculated for the passability of wheeled vehicles

  • The abovementioned text has described the structure of the optimal movement route model implemented in the Tactical Decision Support System (TDSS) and its further possible use within the Maneuver Control System application

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Summary

Introduction

Over the past 10 years, a considerable progress has been observed in the field of autonomous systems in various fields of activity. Outside the network of paths, the vector model navigates directly to the target, without any analysis of the influence of the vegetation and the relief Another planning strategy of movement for autonomous vehicles can be a “potential field” consisting of a limited space of artificial potential values. An important feature of each raster cell is its value (attribute), which is specified by a particular or continuous character of the represented terrain area. It may be a landform, a terrain slope, weather effects, the enemy activity, or the time of its covering in a predetermined manner. The enemy activity is the worst predictable part of the model due to its uncertainty and variant design

Model concept
Elevation layer
Weather layer
The enemy situation layer
Friendly forces and equipment layer
Combined cost surface of passability
Possibilities of the enemy activity influence
Influence of detected enemy combat activity in the past
Influence of the current deployment of enemy forces and equipment
Databases of system information
Simulation of movement executed by enemy forces and equipment
Testing and verification of model functions
Experiment No 1
Maneuver control system
Experiment No 3
Evaluation of experiment No 3
Conclusion

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