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Multi-robot systems have become very popular in recent years because of theirwide spectrum of applications, ranging from surveillance to cooperative payloadtransportation. Model Predictive Control (MPC) is a promising controller formulti-robot control because of its preview capability and ability to handleconstraints easily. The performance of the MPC widely depends on manyparameters, among which the prediction horizon is the major contributor.Increasing the prediction horizon beyond a limit drastically increases thecomputation cost. Tuning the value of the prediction horizon can be verytime-consuming, and the tuning process must be repeated for every task.Moreover, instead of using a fixed horizon for an entire task, a better balancebetween performance and computation cost can be established if differentprediction horizons can be employed for every robot at each time step. Further,for such variable prediction horizon MPC for multiple robots, on-demandcollision avoidance is the key requirement. We propose Versatile On-demandCollision Avoidance (VODCA) strategy to comply with the variable horizon modelpredictive control. We also present a framework for learning the predictionhorizon for the multi-robot system as a function of the states of the robotsusing the Soft Actor-Critic (SAC) RL algorithm. The results are illustrated andvalidated numerically for different multi-robot tasks.

RL-based Variable Horizon Model Predictive Control of Multi-Robot Systems using Versatile On-Demand Collision Avoidance