  In this paper we propose a technique that assigns obstacles to clusters used
for collision avoidance via Mixed-Integer Programming. This strategy enables a
reduction in the number of binary variables used for collision avoidance, thus
entailing a decrease in computational cost, which has been a hindrance to the
application of Model Predictive Control approaches with Mixed-Integer
Programming formulations in real-time. Moreover, the assignment of obstacles to
clusters and the sizes of the clusters are decided within the same optimization
problem that performs the trajectory planning, thus yielding optimal cluster
choices. Simulation results are presented to illustrate an application of the
proposal.
