Multi-Vehicle Trajectory Optimisation On Road Networks

TL;DR

This paper presents a MILP-based method for planning time-optimal trajectories for multiple vehicles on road networks, effectively handling interactions at intersections and outperforming heuristic methods in avoiding local minima.

Contribution

It introduces an MILP formulation with targeted collision constraints for multi-vehicle trajectory optimization, improving computational efficiency and solution optimality.

Findings

MILP achieves globally optimal trajectories avoiding local minima.

Heuristic method scales better with problem size.

Proposed method outperforms heuristic in solution quality.

Abstract

This paper addresses the problem of planning time-optimal trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle interactions at intersections create non-trivial decisions, with complex flow-on effects for subsequent interactions. A globally optimal, minimum time trajectory is found for all vehicles using Mixed Integer Linear Programming (MILP). Computational performance is improved by minimising binary variables using iteratively applied targeted collision constraints, and efficient goal constraints. Simulation results in an open-pit mining scenario compare the proposed method against a fast heuristic method and a reactive approach based on site practices. The heuristic is found to scale better with problem size while the MILP is able to avoid local minima.