Path Planning for Autonomous Heavy Duty Vehicles using Nonlinear Model Predictive Control
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Ruttplanering för tunga autonoma fordon med olinjär modellbaserad prediktionsreglering (Swedish)
In the future autonomous vehicles are expected to navigate independently and manage complex traffic situations. This thesis is one of two theses initiated with the aim of researching which methods could be used within the field of autonomous vehicles. The purpose of this thesis was to investigate how Model Predictive Control could be used in the field of autonomous vehicles. The tasks to generate a safe and economic path, to re-plan to avoid collisions with moving obstacles and to operate the vehicle have been studied. The algorithm created is set up as a hierarchical framework defined by a high and a low level planner. The objective of the high level planner is to generate the global route while the objectives of the low level planner are to operate the vehicle and to re-plan to avoid collisions. Optimal Control problems have been formulated in the high level planner for the use of path planning. Different objectives of the planning have been investigated e.g. the minimization of the traveled length between the start and the end point. Approximations of the static obstacles' forbidden areas have been made with circles. A Quadratic Programming framework has been set up in the low level planner to operate the vehicle to follow the high level pre-computed path and to locally re-plan the route to avoid collisions with moving obstacles. Four different strategies of collision avoidance have been implemented and investigated in a simulation environment.
Place, publisher, year, edition, pages
2013. , 74 p.
Path Planning, MPC, NMPC, Autonomous Vehicle, Optimal Control, Move Blocking
IdentifiersURN: urn:nbn:se:liu:diva-95547ISRN: LiTH-ISY-EX--13/4707--SEOAI: oai:DiVA.org:liu-95547DiVA: diva2:635955
Scania CV AB
Subject / course
Nielsen, Isak, Ph.D. Student
Axehill, Daniel, Assistant Professor