Chaos Generation for Multi-Robot 3D-Volume Coverage Maximization

The utilization of Multi-Robot Systems (MRS) is increasingly touching different aspects. The problem of 3D-Volume coverage is one of the most important problems addressed by teams of MRS, as it is useful in applications such as explorations as well as search and rescue. On another hand, chaos theory is widely used for different applications in multiple disciplines. In this study, we utilize the chaos generation concept in order to produce the paths to be followed by a team of Unmanned Aerial Vehicles (UAVs) to maximize their 3D-volume coverage capacity. Three approaches are investigated; Arnold Equation (AE), Constrained AE and Chaotic Target Points Sorting (CTPS). Simulation results reflected that the CTPS approach outperformed the other two, and a team of 5 UAVs implementing the CTPS concept was able to cover 88% of the target volume. Further investigations include balancing the loading between the UAVs for better energy consumption.

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