Firefly Eindhoven - Control and Coordination

DRAFT

Trajectory tracking

This strategy is the basic method used by the team to conduct the show with the drone. All reference points are stored on the drone’s memory and are fed to the high level controller. The rate with which the points are fed to the controller can be tuned with the gain defined by the user in the simulation, however, it is also proportionally dependent on the rate of the CPU and of the simulation. Therefore it must be emphasized that points’ density and simulation frequency affect the speed of the trajectory directly. The main advantage of the trajectory tracking is simplicity, however, it is highly susceptible controller tuning. With poor controller the drone may skip parts of the trajectory when lagging behind, have significant overshoot or oscillate around points within the trajectory. So far the team has been able to achieve satisfying results and perform a show at the event with moderately tuned controller. It has been noticed that feed forward and lower derivative gains of the high level controller improve significantly the performance of trajectory. The position measurements show that shapes like circles are particularly difficult for the trajectory tracking resulting in ellipses. This is probably due to poor speed estimation when working with the decawave positioning system. Overall the tests have shown that trajectory tracking strategy suffices for the current speed and shape of the trajectories designed for the Glow event. [show results below]

Path following

The path following strategy is a strategy based on the speed control. The supervisor knowing the estimated position of the drone finds the closest point at the trajectory and undertakes two control actions. It calculates force tangential to the trajectory based on reference speed associated with the previously found trajectory index. Secondly it calculates the force towards the trajectory to reduce the deviation from the desired position on the trajectory. This strategy was mainly tested on the ground robots due to instability during the flying tests and different priorities. After two attempts of introducing the path following with the drones it has been asserted that this strategy is too sensitive to noise in the speed estimation. It is recommended to try out this strategy after dealing with the noise e.g. by applying filter to the decawave system as the results with ground robots have shown much better tracking performance with the path following method.

Collision avoidance

Currently still tested approach is the repulsive force method. Based on the distance from the estimated position and the undesired point (static obstacle or other drone) the repulsive force is calculated. The unit vector calculated as the repulsion direction is scaled linearly with the distance(inversely proportional), proportional gain and is divided by the range at which the collision avoidance should start acting. The proportional gain defines the slope of the relation, however, it should be noted that the maximum force is saturated after adding forces with the position control’s action, thus currently it is not designed to deal with operation in saturation.