An Optimization-Based Receding Horizon Trajectory Planning Algorithm
Autor: | Oskar Ljungqvist, Kristoffer Bergman, Daniel Axehill, Torkel Glad |
---|---|
Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Dynamical systems theory Optimal Control Computer science Horizon 020208 electrical & electronic engineering Trailer Trajectory & Path Planning 02 engineering and technology Control Engineering Autonomous Vehicles Upper and lower bounds Set (abstract data type) 020901 industrial engineering & automation Reglerteknik Optimization and Control (math.OC) Control and Systems Engineering Convergence (routing) FOS: Mathematics 0202 electrical engineering electronic engineering information engineering Trajectory Motion planning Mathematics - Optimization and Control Algorithm |
Zdroj: | IFAC-PapersOnLine. 53:15550-15557 |
ISSN: | 2405-8963 |
Popis: | This paper presents an optimization-based receding horizon trajectory planning algorithm for dynamical systems operating in unstructured and cluttered environments. The proposed approach is a two-step procedure that uses a motion planning algorithm in a first step to efficiently find a feasible, but possibly suboptimal, nominal solution to the trajectory planning problem where in particular the combinatorial aspects of the problem are solved. The resulting nominal trajectory is then improved in a second optimization-based receding horizon planning step which performs local trajectory refinement over a sliding time window. In the second step, the nominal trajectory is used in a novel way to both represent a terminal manifold and obtain an upper bound on the cost-to-go online. This enables the possibility to provide theoretical guarantees in terms of recursive feasibility, objective function value, and convergence to the desired terminal state. The established theoretical guarantees and the performance of the proposed algorithm are verified in a set of challenging trajectory planning scenarios for a truck and trailer system. Comment: Submitted for IFAC World Congress 2020 |
Databáze: | OpenAIRE |
Externí odkaz: |