Tractor-Trailer Vehicle Trajectory Planning in Narrow Environments With a Progressively Constrained Optimal Control Approach

Li, Bai; Acarman, Tankut; Zhang, Youmin; Zhang, Liangliang; Yaman, Cagdas; Kong, Qi

doi:10.1109/tiv.2019.2960943

Cited by 67 publications

(29 citation statements)

References 39 publications

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“…Bergman et al [20] divided a searched path into segments and solved several subtle OCPs with the two-point boundary conditions of each segment. Li et al [10] proposed an extended hybrid A* to find a coarse path and then solved a series of OCPs with increasing sizes of obstacles toward their nominal ones. None of these studies attempts to simplify the intractably scaled collision-avoidance constraints, thus resulting in a long runtime.…”

Section: A Related Workmentioning

confidence: 99%

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Acarman

et al. 2022

IEEE Robot. Autom. Lett.

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This study is focused on the maneuver planning problem for a tractor-trailer vehicle in a curvy and tiny tunnel. Due to the curse of dimensionality, the prevalent sampling-andsearch-based planners used to handle a rigid-body vehicle well become less efficient when the trailer number grows or when the tunnel narrows. This fact also has impacts on an optimizationbased planner if it counts on a sampling-and-search-based initial guess to warm-start. We propose an optimization-based maneuver planner that weakly relies on the sampling and search, hoping to get rid of the curse of dimensionality and thus find optima rapidly. The proposed planner comprises three stages: stage 1 identifies the homotopy class via A* search in a 2D grid map; stage 2 recovers the kinematic feasibility with softened intermediate problems iteratively solved; stage 3 finds an optimum that strictly satisfies the nominal collision-avoidance constraints. Optimization-based planners are commonly known to run slowly, but this work shows that they have obvious advantages over the prevalent sampling-and-search-based planners when the solution space dimension is high and/or the constraints are harsh.

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Section: A Related Workmentioning

confidence: 99%

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Acarman

et al. 2022

IEEE Robot. Autom. Lett.

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“…Typically, the prevalent trajectory planners suitable for parking in an unstructured environment may be classified into sampling-based and optimization-based categories [3]. The sampling-based methods can be further classified by sampling the input space or state space.…”

Section: Introductionmentioning

confidence: 99%

“…A sampling-based method and an optimization-based method work better when they are combined [3,[10][11][12][13]. Concretely, a sampling-based method provides a resolutionfeasible coarse path/trajectory, which serves as the initial guess in solving an optimization problem via a local optimizer.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Parking Trajectory Planning With Tiny Passages: A Combination of Multistage Hybrid A-Star Algorithm and Numerical Optimal Control

Sheng

Zhong

2021

IEEE Access

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This paper introduces an autonomous parking trajectory planning method in an unstructured environment with narrow passages. The proposed planner consists of two layers, namely a graph search layer and a numerical optimization layer. The contribution mainly lies in the graph search layer, wherein a multi-stage hybrid A* algorithm is proposed to handle narrow passages formed by obstacles in a tiny or cluttered environment. In the multi-stage hybrid A* algorithm, 2-dim A* algorithm is conducted to find a global route that connects the starting point and the goal point. Along the derived global route, subtle segments that traverse narrow passages will be extracted. Thereafter, the hybrid A* algorithm is used to plan kinematically feasible sub-paths that connect the boundary points of each subtle segment. The hybrid A* algorithm is also used to find the linking paths that connect adjacent sub-paths. Combing all of the subpaths and linking paths in a sequence yields a coarse path, which is converted into a coarse trajectory if attaching a time-optimal velocity profile to it. The coarse trajectory is fed into the numerical optimization layer as the initial guess. Simulation results indicate that our two-layer planner is much faster than the prevalent ones in dealing with unstructured environments with narrow passages.

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“…However, previous controllers 34,35 could not guarantee the transient and steady‐state performance characteristics in the presence of model uncertainties and nearly all the previous controllers need velocity signals in practice. After a deep review of the literature on the planning and control of tractor‐trailers including References 1‐41, there exist the following major concerns regarding the design of tracking controllers for such vehicles:The coordinated leader‐following formation control problem of multiple tractors with n ‐trailers has not been addressed in the literature yet 18‐41 which is very useful in the coordinated intelligent transportation systems such as crop harvesting operations. The most of proposed controllers are only applied to a tractor with a single trailer at the kinematic level. There are few works to address the control problem of tractor‐trailers by taking both kinematic and dynamic models into account. The available dynamic controllers definitely require exact linear and angular velocities of the tractor‐trailer in real‐time for the feedback which may be unavailable in practical situations. Such vehicles suffer from the nonholonomic constraints which make the design of the controller more complicated for their general form.…”

Section: Introductionmentioning

confidence: 99%

“…The subject of this article is focused on solving the above shortcomings which have not been addressed sufficiently to the best of author's knowledge. According to a detailed literature review, 1‐41 main novelties and contributions of this article are clearly stated as follows:An observer‐based adaptive robust neural network leader‐following formation controller is proposed for N tractors each of which connected to n ‐trailers by taking both general forms of kinematic and dynamic models in the presence of model uncertainties which has not been addressed in the literature yet 1‐41 Compared with all previous works including References 18‐41, the proposed leader‐following formation controller ensures prescribed transient and steady‐state performance characteristics including convergence rate, undershoot/overshoot, and steady‐state error in order to provide a smooth transient response without unwanted large overshoots. The most of previous works that employ prescribed performance technique in the literature generally rely on the backstepping design procedure…”

Section: Introductionmentioning

confidence: 99%

Saturated observer‐based adaptive neural network leader‐following control of N tractors with n‐trailers with a guaranteed performance

Shojaei

Abdolmaleki

2020

Adaptive Control & Signal

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This article addresses the leader‐following neural network adaptive observer‐based control of N tractors connected to n trailers with the prescribed performance specifications. To propose the controller, a change of coordinates and a nonlinear error transformation are used to transform the constrained error dynamics to a new second‐order Euler‐Lagrange unconstrained error dynamics which inherits all structural properties of ith vehicle dynamic model. By combining a projection‐type neural network and an adaptive robust technique, a novel leader‐following saturated output‐feedback controller is proposed to force that ith vehicle tracks a virtual leader trajectory with the prescribed transient and steady‐state characteristics while reducing the actuator saturation risk and compensating all unknown dynamic model parameters, external disturbances, unmolded dynamics, and NN approximation errors. A saturated velocity observer is heuristically proposed to obviate the requirement for the velocity measurements of ith vehicle without any unwanted peaking. A Lyapunov‐based stability analysis is utilized to prove that all the tracking and state observation errors are semi‐globally uniformly ultimately bounded (SGUUB) and they converge to small bounds including the origin with a prescribed performance. At the end, computer simulations will be shown to validate the efficacy of the proposed controller in practice.

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Tractor-Trailer Vehicle Trajectory Planning in Narrow Environments With a Progressively Constrained Optimal Control Approach

Cited by 67 publications

References 39 publications

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Optimization-Based Maneuver Planning for a Tractor-Trailer Vehicle in a Curvy Tunnel: A Weak Reliance on Sampling and Search

Autonomous Parking Trajectory Planning With Tiny Passages: A Combination of Multistage Hybrid A-Star Algorithm and Numerical Optimal Control

Saturated observer‐based adaptive neural network leader‐following control of N tractors with n‐trailers with a guaranteed performance

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