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

TL;DR

This paper introduces a trajectory planning method that clusters dynamic obstacles using Mixed-Integer Linear Programming to reduce computational costs and enable real-time collision avoidance.

Contribution

It presents a novel obstacle clustering strategy integrated into trajectory planning via MIP, optimizing cluster assignments and sizes within the planning process.

Findings

Reduced number of binary variables in MIP models

Lower computational cost for real-time collision avoidance

Effective obstacle clustering demonstrated in simulations

Abstract

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.