This is a different approach from the generic approach described for UAV swarm flight path management in uncharted territory as described by an earlier article1 using waypoint selections and trajectory smoothing. There are two key differentiating characteristics of UAV swarms that can provide additional insights. One is that the UAV swarm is not necessarily always a one-follow-another sequence through waypoints and trajectory. In fact the formation is dynamic and the center of the UAV swarm has its own position, altitude, velocity and distance from references than the leading and trailing units. Another is that the units are themselves capable of carrying a variety of sensors such as LiDAR data that can be stored and queried and in the cloud to build the knowledge base of the landscape via data processing systems similar to image capturing ones discussed earlier2. The commercial UAV swarm serving cloud-software based solution3 does not limit the types and capabilities of the drones or the storing and querying of the data from the sensors. The generic approach treated each unit of the UAV swarm as a co-ordinate but this approach outlined below makes use of both these factors.

The center of the UAV swarm regardless of its formation and changes to formations when following trajectories through waypoints can also be tracked in addition to each unit of the swarm. The scatter plots of position vs time, altitude vs time, speed vs time, distance from reference point versus time, inter UAV distance vs time and velocity components versus time demonstrate elongated scatter plots which lends itself to correlation. Therefore, logistic regression can be used to predict various aspects of the flight path of the center of the UAV swarm. Logistic regression differs from the other Regression techniques in the use of statistical measures. Regression is very useful to calculate a linear relationship between a dependent and independent variable, and then use that relationship for prediction. This technique is suitable for specific aspects of the flight path for the center of the UAV swarm that doesn’t always have to be the same drone. One advantage of logistic regression is that the algorithm is highly flexible, taking any kind of input, and supports several different analytical tasks. Use of MicrosoftML rxFastTree is recommended for this purpose.

The gradient boost algorithm for rxFastTree is possible with several loss functions including the squared loss function. The algorithm for the least squares regression can be written as :

1. Set the initial approximation

2. For a set of successive increments or boosts each based on the preceding iterations, do

3. Calculate the new residuals

4. Find the line of search by aggregating and minimizing the residuals

5. Perform the boost along the line of search

6. Repeat 3,4,5 for each of 2.