Liang Gao, Bobo Jia, Daiwei Li, Yi Yang, Shanshan Xie
{"title":"Efficient safety-critical trajectory planning for any N-trailer system with a general model","authors":"Liang Gao, Bobo Jia, Daiwei Li, Yi Yang, Shanshan Xie","doi":"10.1016/j.conengprac.2025.106287","DOIUrl":null,"url":null,"abstract":"<div><div>Trajectory planning for tractor–trailer vehicles (TTVs) in a cluttered environment is a highly challenging task owing to complicated kinematic and large-scale collision-avoidance constraints. It has stringent requirements for trajectory feasibility and computational efficiency. Moreover, the varying configurations of TTVs pose challenges to the scalability of the planning method. This article proposes a novel safety-critical trajectory planning method with a general model to address these challenges. Firstly, an algebraic general model is first presented to represent these N-Trailer systems with different hitching types and trailer types uniformly. Secondly, the planning problem is formulated as a nonlinear model predictive control scheme with two key efforts to accelerate calculation speed. One operation is that a novel search-guided optimization-based collision avoidance (SG-OBCA) method is developed to provide a high-quality initial guess. The other operation is that intractable non-convex collision-avoidance constraints are translated into a dual form based on exponential discrete-time control barrier function (DCBF). Finally, both comparative simulations and real-world experiments are conducted to demonstrate the efficiency and applicability of the proposed method in different complicated scenarios and configurations of TTVs.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"158 ","pages":"Article 106287"},"PeriodicalIF":5.4000,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066125000504","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Trajectory planning for tractor–trailer vehicles (TTVs) in a cluttered environment is a highly challenging task owing to complicated kinematic and large-scale collision-avoidance constraints. It has stringent requirements for trajectory feasibility and computational efficiency. Moreover, the varying configurations of TTVs pose challenges to the scalability of the planning method. This article proposes a novel safety-critical trajectory planning method with a general model to address these challenges. Firstly, an algebraic general model is first presented to represent these N-Trailer systems with different hitching types and trailer types uniformly. Secondly, the planning problem is formulated as a nonlinear model predictive control scheme with two key efforts to accelerate calculation speed. One operation is that a novel search-guided optimization-based collision avoidance (SG-OBCA) method is developed to provide a high-quality initial guess. The other operation is that intractable non-convex collision-avoidance constraints are translated into a dual form based on exponential discrete-time control barrier function (DCBF). Finally, both comparative simulations and real-world experiments are conducted to demonstrate the efficiency and applicability of the proposed method in different complicated scenarios and configurations of TTVs.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.