Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios

Funke, Joseph; Brown, Matthew; Erlien, Stephen M.; Gerdes, J. Christian

doi:10.1109/tcst.2016.2599783

Cited by 323 publications

(197 citation statements)

References 18 publications

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“…Forward invariance ofC for the proposed method is formally guaranteed in the following theorem: Theorem 1. Consider the controllable system (1). LetĈ j be defined by (2), (3), for the twice-continuously differentiable, relative degree two functions h j : D j → R, defined on the open set D j ⊃ C j ⊃Ĉ j .…”

Section: A Robust Barrier Functions For Relative Degree Two Systemsmentioning

confidence: 99%

“…The control of these systems is usually applied as a force/torque at the acceleration level, and most, if not all, mechanical systems must satisfy workspace constraints. Examples include collision avoidance of autonomous vehicles in traffic [1], a robotic manipulator avoiding obstacles in the environment [2], and of particular note here is a robotic hand manipulating an object [3]. Workspace constraints are position dependent, which due to the second order dynamics, makes them relative degree two.…”

Section: Introductionmentioning

confidence: 99%

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Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Cortez

Oetomo

Manzie

et al. 2021

IEEE Trans. Contr. Syst. Technol.

104

101

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Control barrier functions have been demonstrated to be a useful method of ensuring constraint satisfaction for a wide class of controllers, however existing results are mostly restricted to continuous time systems of relative degree one. Mechanical systems, including robots, are typically second-order systems in which the control occurs at the force/torque level. These systems have velocity and position constraints (i.e. relative degree two) that are vital for safety and/or task execution. Additionally, mechanical systems are typically controlled digitally as sampleddata systems. The contribution of this work is two-fold. First, is the development of novel, robust control barrier functions that ensure constraint satisfaction for relative degree two, sampleddata systems in the presence of model uncertainty. Second, is the application of the proposed method to the challenging problem of robotic grasping in which a robotic hand must ensure an object remains inside the grasp while manipulating it to a desired reference trajectory. A grasp constraint satisfying controller is proposed that can admit existing nominal manipulation controllers from the literature, while simultaneously ensuring no slip, no over-extension (e.g. singular configurations), and no rolling off of the fingertips. Simulation and experimental results validate the proposed control for the robotic hand application.

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Section: A Robust Barrier Functions For Relative Degree Two Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Cortez

Oetomo

Manzie

et al. 2021

IEEE Trans. Contr. Syst. Technol.

104

101

View full text Add to dashboard Cite

show abstract

“…As mentioned, the state variable x 1 is readily available by (7). However, it is difficult to measure the state x 2 directly, so an estimation method should be considered to fully exploit the developed model.…”

Section: B Control Oriented Modelmentioning

confidence: 99%

“…and x 1 = α f − α r , that is defined by (7). Now, the following control law can be derived by substituting (7) and (4) into (29):…”

Section: B Comparison Between Proposed and Conventional Controllersmentioning

confidence: 99%

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Han

Park

Sankar³

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety's sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach. Index Terms-Model Predictive Control, Constrained Control, Vehicle Handing Characteristics, and Cornering Performance. Seibum B. Choi(M'09) received the B.S. degree in mechanical engineering from Seoul National University, Seoul, Korea, in 1985, the M.S.

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“…Information regarding the future environment, specifically the behavior of other dynamic agents, would facilitate planning a trajectory proactively to a rapidly changing environment [2]. For instance, in the context of autonomous vehicles, accurate environment prediction would provide for a smoother user experience, with fewer sharp maneuvers, while ensuring passenger safety from possible moving hazards on the road [3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning

Itkina

Driggs-Campbell

Kochenderfer

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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A key challenge for autonomous driving is safe trajectory planning in cluttered, urban environments with dynamic obstacles, such as pedestrians, bicyclists, and other vehicles. A reliable prediction of the future environment, including the behavior of dynamic agents, would allow planning algorithms to proactively generate a trajectory in response to a rapidly changing environment. We present a novel framework that predicts the future occupancy state of the local environment surrounding an autonomous agent by learning a motion model from occupancy grid data using a neural network. We take advantage of the temporal structure of the grid data by utilizing a convolutional long-short term memory network in the form of the PredNet architecture. This method is validated on the KITTI dataset and demonstrates higher accuracy and better predictive power than baseline methods. This work was supported by the Ford-Stanford Alliance. 1 M. Itkina and M. J. Kochenderfer are with the

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Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios

Cited by 323 publications

References 18 publications

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Grasping

Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning

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