Waypoint trajectory planning in the presence of obstacles with a tunnel-MILP approach

This work presents a waypoint trajectory planning technique for an autonomous vehicle in the presence of obstacles using a tunnel-MILP formulation for the avoidance constraints. Predictive Control is used to address the issues of dynamic constraint satisfaction and obstacle avoidance. However, the complexity of the optimization problem to be solved may escalate as the number of obstacles increases. To circumvent this issue, a tunnel-MILP approach is employed. Even so, the optimization problem may still be too complex, i. e., involve a large number of decision variables, to be computationally tractable within the relatively small sample time required by vehicle guidance applications. The number of decision variables is reduced via the pre-computation of waypoints during an offline trajectory planning phase. In this manner, during the online control phase, the optimization problem to be solved needs only to compute a control solution to reach the next waypoint in the sequence, instead of the whole control solution to reach the target set from the current position. Simulation results are presented to show that the employment of the waypoint trajectory planning technique brings about benefits regarding the computational burden associated to the solution of the online optimal control problem.