Optimizing Fuel-Constrained UAV-UGV Routes for Large Scale Coverage: Bilevel Planning in Heterogeneous Multi-Agent Systems

Fast moving unmanned aerial vehicles (UAVs) are well suited for aerial surveillance, but are limited by their battery capacity. To increase their endurance UAVs can be refueled on slow moving unmanned ground vehicles (UGVs). The cooperative routing of UAV-UGV multi-agent system to survey vast regions within their speed and fuel constraints is a computationally challenging problem, but can be simplified with heuristics. Here we present multiple heuristics to enable feasible and sufficiently optimal solutions to the problem. Using the UAV fuel limits and the minimum set cover algorithm, the UGV refueling stops are determined. These refueling stops enable the allocation of mission points to the UAV and UGV. A standard traveling salesman formulation and a vehicle routing formulation with time windows, dropped visits, and capacity constraints is used to solve for the UGV and UAV route, respectively. Experimental validation on a small-scale testbed (http://tiny.cc/8or8vz) underscores the effectiveness of our multi-agent approach.Comment: The paper is submitted to MRS 202

Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it

Modelling of Artificial Neural Network to control the cooling rate of a Laboratory Scale Run-Out Table

Run Out Tables (ROTs) have been used for long time in order to achieve different microstructure of steel in the industries. The microstructure of steel controlled by the cooling rate which in turn depends on various factors like the plate velocity, nozzle bank distance, coolant flow rate, and many others. Achieving new steel grade thus demand a proper combination setting of all such parameters. The observed data like upper nozzle distance, lower nozzle distance and mass flow rate of coolant from the laboratory scale ROTs are used to find out the cooling rate which is important parameter for achieving desired properties in steel. An Artificial Neural Network has been used here to creating an empirical relation between the observed data and thermodynamics parameter which will determine the cooling rate and validate it