Multi-robot systems have become very popular in recent years because of their
wide spectrum of applications, ranging from surveillance to cooperative payload
transportation. Model Predictive Control (MPC) is a promising controller for
multi-robot control because of its preview capability and ability to handle
constraints easily. The performance of the MPC widely depends on many
parameters, among which the prediction horizon is the major contributor.
Increasing the prediction horizon beyond a limit drastically increases the
computation cost. Tuning the value of the prediction horizon can be very
time-consuming, and the tuning process must be repeated for every task.
Moreover, instead of using a fixed horizon for an entire task, a better balance
between performance and computation cost can be established if different
prediction horizons can be employed for every robot at each time step. Further,
for such variable prediction horizon MPC for multiple robots, on-demand
collision avoidance is the key requirement. We propose Versatile On-demand
Collision Avoidance (VODCA) strategy to comply with the variable horizon model
predictive control. We also present a framework for learning the prediction
horizon for the multi-robot system as a function of the states of the robots
using the Soft Actor-Critic (SAC) RL algorithm. The results are illustrated and
validated numerically for different multi-robot tasks