Obstacle avoidance in real time with Nonlinear Model Predictive Control of autonomous vehicles

Abbas, Muhammad A.; Milman, Ruth; Eklund, Johan

doi:10.1109/ccece.2014.6901109

Cited by 29 publications

(25 citation statements)

References 13 publications

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“…For this reason, simplified models are very often preferred. Kinematic bicycle (or singletrack) models [9], [10], [19], or even simpler second-order integrator models [2] are therefore common in the trajectory planning literature.…”

Section: Constrained Second-order Integrator Modelmentioning

confidence: 99%

“…Therefore, the existing literature is generally divided between medium-term (a few seconds) trajectory planning including obstacle avoidance for low-speed applications, mainly relying on simple kinematic models (see, e.g. [9], [10]), and short-term (sub-second) trajectory tracking for high-speed or low-adherence applications using wheel dynamics modeling (see, e.g., [11]- [14]). In the second case, obstacle avoidance is generally not considered (with the notable exception of [15]), and the existence of a feasible collision-free trajectory is not guaranteed in the case of an unexpected obstacle.…”

Section: Introductionmentioning

confidence: 99%

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High-speed trajectory planning for autonomous vehicles using a simple dynamic model

Altché

Polack

Fortelle

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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To improve safety and energy efficiency, autonomous vehicles are expected to drive smoothly in most situations, while maintaining their velocity below a predetermined speed limit. However, some scenarios such as low road adherence or inadequate speed limit may require vehicles to automatically adapt their velocity without external input, while nearing the limits of their dynamic capacities. Many of the existing trajectory planning approaches are incapable of making such adjustments, since they assume a feasible velocity reference is given. Moreover, near-limits trajectory planning often implies high-complexity dynamic vehicle models, making computations difficult. In this article, we use a simple dynamic model derived from numerical simulations to design a trajectory planner for high-speed driving of an autonomous vehicle based on model predictive control. Unlike existing techniques, our formulation includes the selection of a feasible velocity to track a predetermined path while avoiding obstacles. Simulation results on a highly precise vehicle model show that our approach can be used in real-time to provide feasible trajectories that can be tracked using a simple control architecture. Moreover, the use of our simplified model makes the planner more robust and yields better trajectories compared to kinematic models commonly used in trajectory planning.

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Section: Constrained Second-order Integrator Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

Altché

Polack

Fortelle

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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“…Nonlinear Model Predictive Control (NMPC) has proven successful results and has been employed in many recent publications [4,5] because it combines both control and collision avoidance into one algorithm which can be used for critical maneuvers. It is an optimization-based controller that has been widely used for various transportation systems such as autonomous ships [6] and autonomous vehicles [4].…”

Section: Introductionmentioning

confidence: 99%

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Abdelaal

Schön

2020

Algorithms

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This paper considers nonlinear model predictive control for simultaneous path-following and collision avoidance of connected autonomous vehicles. For each agent, a nonlinear bicycle model is used to predict a sequence of the states and then optimize them with respect to a sequence of control inputs. The objective function of the optimal control problem is to follow the planned path which is represented by a Bézier curve. In order to achieve collision avoidance among the networked vehicles, a geometric shape must be selected to represent the vehicle geometry. In this paper, an elliptic disk is selected for that as it represents the geometry of the vehicle better than the traditional circular disk. A separation condition between each pair of elliptic disks is formulated as time-varying state constraints for the optimization problem. Driving corridors are assumed to be also Bézier curves, which could be obtained from digital maps, and are reformulated to suit the controller algorithm. The algorithm is validated using MATLAB simulation with the aid of ACADO toolkit.

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“…They can be classified into two categories [8]. In the first category, motion planning and low-level control are unified into one unique MPC formulation using different vehicle models, such as in [9], [10] and [11]. However, they are not robust to modeling errors, disturbances and parameter uncertainties as they lack a realtime feedback controller (the frequency of recomputing is rather low for low-level control).…”

Section: Introductionmentioning

confidence: 99%

Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

Polack¹,

Altché

d’Andréa-Novel³

et al. 2018

2018 Annual American Control Conference (ACC)

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This paper proposes to combine a 10Hz motion planner based on a kinematic bicycle Model Predictive Control (MPC) and a 100Hz closed-loop Proportional-Integral-Derivative (PID) controller to cope with normal driving situations. Its novelty consists in ensuring the feasibility of the computed trajectory by the motion planner through a limitation of the steering angle depending on the speed. This ensures the validity of the kinematic bicycle model at any time. The architecture is tested on a high-fidelity simulation model on a challenging track with small curve radius, with and without surrounding obstacles.

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Obstacle avoidance in real time with Nonlinear Model Predictive Control of autonomous vehicles

Cited by 29 publications

References 13 publications

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

High-speed trajectory planning for autonomous vehicles using a simple dynamic model

Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles

Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

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