UAV PATROLLING SIMULATOR
The test platform was built using the PyGame library. The scenario is a 50×50 grid containing four UAVs modeled as robot-agents. We present four types of scenarios, one free of obstacles and other three containing different compositions of no-fly zones (NFZ). The aerial robots cannavigate through the scenario, interact with each other, and avoid the NFZ. The starting position of the UAVs is at thelower-left corner of the scenario.
NC Drone
For the UVAs moviments we implemented the NC Drone algorithm. The NC is an RTSM which guides biologically-inspired robots over a grid-discretized scenario by reading and writing pheromones for motion planning.
The primary goal of the NC-Drone is to reduce excessive NTM while keeping unpredictable behavior by solving the tie issue. NC-Drone checks the position of the cells with the same minimum-value. In the case of one of these cells is following the sweeping direction, this cell is picked as the next spot to be explored. In this way, the aerial vehicle maintains a straight trajectory. If there is no such cell, a random decision is made, and the UAV performs a turn to reach the selected side cell.
Genetic Approach
We use Genetic Algorithm (GA) optimization to define the values of the set of parameters for the patrolling strategies.
pip install gaft
Simulation Scenario
No Fly Zone Scenarios
