Accurate Path Tracking by Adjusting Look-Ahead Point in Pure Pursuit Method

Ahn, Joonwoo; Shin, Seokmin; Kim, Min Soo; Park, Jaeheung

doi:10.1007/s12239-021-0013-7

Cited by 61 publications

(26 citation statements)

References 15 publications

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“…However, there are some shortcomings in this approach in the empirical tuning of the look-ahead parameters which can have a strong effect on performance. Future work could include incorporating an optimized look-ahead distance such as in [11] or [8]. Additionally this method does not truly incorporate the ability to handle changing conditions outside of feedback once disturbances occur.…”

Section: G Discussionmentioning

confidence: 99%

Robust Modeling and Controls for Racing on the Edge

Spisak¹,

Saba²,

Suvarna³

et al. 2022

Preprint

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Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the need for improved dynamics modeling and learning.

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Section: G Discussionmentioning

confidence: 99%

Robust Modeling and Controls for Racing on the Edge

Spisak¹,

Saba²,

Suvarna³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To collect training data, the expert selects the lookahead point a exp,t ∈ {a exp,t x , a exp,t y }, and the vehicle is controlled to reach the selected look-ahead point in realtime. The steering angle command is calculated with the pure pursuit algorithm [34]. The velocity command is proportional to the distance between the look-ahead point and the vehicle.…”

Section: Datasetmentioning

confidence: 99%

“…The pure pursuit algorithm [34] was used to calculate the steering angle command (δ ) to reach the look-ahead point:…”

Section: Vehicle and Hardwarementioning

confidence: 99%

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Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning

Ahn¹,

Kim²,

Park³

2022

Preprint

Self Cite

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Unstructured environments are difficult for autonomous driving. This is because various unknown obstacles are lied in drivable space without lanes, and its width and curvature change widely. In such complex environments, searching for a path in real-time is difficult. Also, inaccurate localization data reduce the path tracking accuracy, increasing the risk of collision. Instead of searching and tracking the path, an alternative approach has been proposed that reactively avoids obstacles in real-time. Some methods are available for tracking global path while avoiding obstacles using the candidate paths and the artificial potential field. However, these methods require heuristics to find specific parameters for handling various complex environments. In addition, it is difficult to track the global path accurately in practice because of inaccurate localization data. If the drivable space is not accurately recognized (i.e., noisy state), the vehicle may not smoothly drive or may collide with obstacles. In this study, a method in which the vehicle drives toward drivable space only using a vision-based occupancy grid map is proposed. The proposed method uses imitation learning, where a deep neural network is trained with expert driving data. The network can learn driving patterns suited for various complex and noisy situations because these situations are contained in the training data. Experiments with a vehicle in actual parking lots demonstrated the limitations of general model-based methods and the effectiveness of the proposed imitation learning method.

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“…One of the wellknown methods of controlling the self-driving cars is the «pure pursuit» method 1,2 . This method is used to control mobile robots 3 [2,3,4], unmanned vehicles 4,5 [5], agricultural machines [6], logging harvesters [7], underwater uninhabited vehicles [8], etc. However, there are no studies of this method when driving a grader.…”

Section: Introductionmentioning

confidence: 99%

Pure pursuit method use to control unmanned motor grader

Sukharev

2022

Vestn. SibADI

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Introduction. A relevant objective of implementing the advanced systems of self-driving road construction vehicles can be accomplished by mathematical modelling. One of the important issues when creating a motion control system for a self-driving vehicle is to develop a trajectory following algorithm. The most well-known method of following the trajectory is a pure pursuit method, which is successfully used to control the movement of mobile robots.Materials and methods. Hence, the research objective has been defined and is to adapt the pure pursuit method to control an autonomous grader. To achieve the research objective, the task of a mathematical model of the motor grader movement with front steering wheels has been developed, and a mathematical model of the motor grader motion control system has been compiled. Besides, we propose an integral criterion to evaluate the efficiency of the motion control system of a unmanned grader. Some theoretical studies of the mathematical model have been carried out and the dependencies of the integral criterion on the design and operational parameters of the grader, as well as on the parameter of the control method (visibility range) have been obtained. Moreover, the optimal values of the visibility range for various values of the base length, base coefficient and machine speed have been defined according to the proposed efficiency criterion.Results. As a result of approximating the obtained optimal values, the pure pursuit method has been modified to control a self-driving motor grader, taking into account its design features and travel speed. The results obtained can be used to create the prototypes of unmanned control systems for road construction vehicles.

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Accurate Path Tracking by Adjusting Look-Ahead Point in Pure Pursuit Method

Cited by 61 publications

References 15 publications

Robust Modeling and Controls for Racing on the Edge

Robust Modeling and Controls for Racing on the Edge

Vision-based Autonomous Driving for Unstructured Environments Using Imitation Learning

Pure pursuit method use to control unmanned motor grader

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