Details

Autor(en) / Beteiligte

• Lu, Yang
• Zhang, Xinglong
• Xu, Xin
• Zhang, Wenzhuo

Titel

A Two-layer MPC Approach for Consistent Planning and Control of Autonomous Vehicles

Ist Teil von

• 2022 International Conference on Advanced Robotics and Mechatronics (ICARM), 2022, p.717-724

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• The widely-used algorithms for motion planning and control of autonomous vehicles usually adopt a layered structure. However, consistency between the two levels is critical to obtain high control performance and maneuvering ability. Aiming at this issue, we propose a two-layer model predictive control (MPC) approach for consistent planning and control of autonomous vehicles. In the higher layer, we propose a finite-horizon convex optimization algorithm for time-optimal motion planning using obstacle convexification of non-convex obstacles. In the proposed planner, the vehicle model uncertainty was learned by a radial-basis-function-based (RBF) neural network whose weight is optimized using pre-collected motion datasets. The surrounding obstacles were detected by a radar, and a clustering algorithm was utilized to process the radar point clouds to isolate polygonal obstacles. Also, the obstacle avoidance constraints were convexified with polyhedrons. As such, the planning problem at the higher layer is transformed into a convex optimization problem to be solved. In the lower layer, a model predictive controller is designed to follow the planned trajectory using the learned dynamic model and a similar performance index to the higher layer. Consequently, the inconsistency between the higher layer and the lower layer can be highly reduced and the control performance can be improved compared with the planning scenario with a nominal model. Extensive simulated and experimental results on planning tasks of unstructured environments have been performed to verify the effectiveness of the proposed approach.