Lattice-based motion planning for a general 2-trailer system

Ljungqvist, Oskar; Evestedt, Niclas; Cirillo, Marcello; Axehill, Daniel; Holmer, Olov

doi:10.1109/ivs.2017.7995817

Cited by 51 publications

(67 citation statements)

References 19 publications

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“…The proposed path planning and path-following control framework summarizes and extends our previous work in [38][39][40]. Here, the complete system is implemented on a full-scale test vehicle and results from both simulations and real-world experiments are presented to demonstrate its performance.…”

Section: Introductionmentioning

confidence: 94%

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Ljungqvist

Evestedt²,

Axehill

et al. 2019

Journal of Field Robotics

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Maneuvering a general 2‐trailer with a car‐like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path‐following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice‐based path planning framework is developed in order to generate kinematically feasible and collision‐free paths and a path‐following controller is designed to stabilize the lateral and angular path‐following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car‐like tractor, making the system independent of additional trailer sensors. The proposed path‐planning and path‐following control framework is implemented on a full‐scale test vehicle and results from simulations and real‐world experiments are presented.

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Section: Introductionmentioning

confidence: 94%

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Ljungqvist

Evestedt²,

Axehill

et al. 2019

Journal of Field Robotics

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“…The parallel Riccati algorithm using mpi outperforms the serial Riccati recursion for N 18, which is similar to the results for the Matlab implementations where the communication overhead is neglected. This speed-up can be important in for example optimal control for motion planning problems (LaValle, 2006;Ljungqvist et al, 2017;Bergman and Axehill, 2017), and mhe problems where long horizons are often used (Rao et al, 1998). It can be seen that the parallel Riccati using mpi over ib only requires around 65% − 85% of the total computation time when using the tcp/ip implementation.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In this thesis, the focus is spent on numerical algorithms for increasing the performance in second-order methods for discrete-time optimal control problems in the form of constrained finite-time optimal control (cftoc) problems. This type of problems is an important component in mpc (Maciejowski, 2002), but can also be useful for example in motion planning problems for dynamical systems (LaValle, 2006;Ljungqvist et al, 2017;Bergman and Axehill, 2017) and mhe problems (Rao, 2000;Jørgensen, 2004;. …”

Section: Optimal Controlmentioning

confidence: 99%

Structure-Exploiting Numerical Algorithms for Optimal Control

Nielsen¹

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Cover illustration: An illustration of the structure of the Karush-Kuhn-Tucker matrix for the unconstrained finite-time optimal control problems considered in this thesis. Thank you Jonas Linder for helping me with the design.Linköping studies in science and technology. Dissertations.No. 1848 Tillägnas hela min underbara familj Structure-Exploiting Numerical Algorithms for Optimal Control AbstractNumerical algorithms for efficiently solving optimal control problems are important for commonly used advanced control strategies, such as model predictive control (mpc), but can also be useful for advanced estimation techniques, such as moving horizon estimation (mhe). In mpc, the control input is computed by solving a constrained finite-time optimal control (cftoc) problem on-line, and in mhe the estimated states are obtained by solving an optimization problem that often can be formulated as a cftoc problem. Common types of optimization methods for solving cftoc problems are interior-point (ip) methods, sequential quadratic programming (sqp) methods and active-set (as) methods. In these types of methods, the main computational effort is often the computation of the second-order search directions. This boils down to solving a sequence of systems of equations that correspond to unconstrained finite-time optimal control (uftoc) problems. Hence, high-performing second-order methods for cftoc problems rely on efficient numerical algorithms for solving uftoc problems. Developing such algorithms is one of the main focuses in this thesis. When the solution to a cftoc problem is computed using an as type method, the aforementioned system of equations is only changed by a low-rank modification between two as iterations. In this thesis, it is shown how to exploit these structured modifications while still exploiting structure in the uftoc problem using the Riccati recursion. Furthermore, direct (non-iterative) parallel algorithms for computing the search directions in ip, sqp and as methods are proposed in the thesis. These algorithms exploit, and retain, the sparse structure of the uftoc problem such that no dense system of equations needs to be solved serially as in many other algorithms. The proposed algorithms can be applied recursively to obtain logarithmic computational complexity growth in the prediction horizon length. For the case with linear mpc problems, an alternative approach to solving the cftoc problem on-line is to use multiparametric quadratic programming (mp-qp), where the corresponding cftoc problem can be solved explicitly off-line. This is referred to as explicit mpc. One of the main limitations with mp-qp is the amount of memory that is required to store the parametric solution. In this thesis, an algorithm for decreasing the required amount of memory is proposed. The aim is to make mp-qp and explicit mpc more useful in practical applications, such as embedded systems with limited memory resources. The proposed algorithm exploits the structure from the qp problem in the parametric solution in order to reduc...

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“…Q captures the smoothness behavior, and λ determines the trade-off between time duration and smoothness of a motion [15].…”

Section: State Lattice Constructionmentioning

confidence: 99%

“…As will be shown in this work, for good performance it does not only suffice to optimize the behavior between the boundary conditions, but also the conditions themselves. One approach to select the boundary conditions in the set of BVPs to be solved is by manual specification, which is typically done by an expert of the system [3,15]. The manual procedure can be very time-consuming and if some of the system's parameters are altered, the boundary conditions usually need to be carefully re-selected to not unnecessarily restrict the performance possible to obtain, and to maintain feasibility, during the following optimization of the motion primitives.…”

Section: Introductionmentioning

confidence: 99%

Improved Optimization of Motion Primitives for Motion Planning in State Lattices

Bergman

Ljungqvist

Axehill

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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In this paper, we propose a framework for generating motion primitives for lattice-based motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the end-point boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.

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Lattice-based motion planning for a general 2-trailer system

Cited by 51 publications

References 19 publications

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Structure-Exploiting Numerical Algorithms for Optimal Control

Improved Optimization of Motion Primitives for Motion Planning in State Lattices

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