A Framework for Constrained Deployment Optimization of Wireless Mobile Sensor Networks

The expansion of mobile sensors, like robots and uncrewed aerial vehicles (UAVs), across diverse applications such as remote sensing, monitoring, and communication relay, has been exponential. Yet, ensuring their safe and successful operation depends crucially on optimized deployment tailored to the application requirements while constrained by various limitations. This study focuses on the optimization of robot/UAV trajectories under these constraints. However, implementing constraints poses considerable challenges. To this end, a framework for constrained deployment optimization of wireless robotic swarms is proposed. This framework formulates as a quadratic-programming problem which utilizes Bézier curves to model trajectories and predict their states over a time horizon. Constraints are systematically categorized and embedded in the Bézier curve formulation. This framework offers ease of adoption to various scenarios and flexibility in accommodating different mobile sensor dynamics, constraints, and deployment strategies.