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

Trajectory planning with a dynamic obstacle clustering strategy using Mixed-Integer Linear Programming