Autonomous Vehicle Waypoint Navigation Using Hyper-Clothoids
| dc.contributor.author | Kotha, Bhavi Bharat | en |
| dc.contributor.committeechair | Southward, Steve C. | en |
| dc.contributor.committeechair | Wicks, Alfred L. | en |
| dc.contributor.committeemember | Abbott, A. Lynn | en |
| dc.contributor.committeemember | Leonessa, Alexander | en |
| dc.contributor.department | Mechanical Engineering | en |
| dc.date.accessioned | 2022-01-21T09:00:32Z | en |
| dc.date.available | 2022-01-21T09:00:32Z | en |
| dc.date.issued | 2022-01-20 | en |
| dc.description.abstract | This research study presents two control solutions, PID and the novel hyper-clothoid control strategy, to autonomously navigate a car. These waypoint navigation solutions smoothly connect the given waypoints with C1 continuity using Hermite cubic splines which is used as a reference path for the controller to track. The PID controller uses lateral and heading error to generate a steering profile for the vehicle to track the constructed reference path. A novel real time solution is presented as the second control strategy which involves constructing clothoids to generate a steering profile. The resultant car trajectory preserves curvature and curvature rate continuity. A simulation test bench was developed in MATLAB and Simulink. Six benchmark waypoint datasets have been used for regression testing and validating the algorithms. Both the proposed control strategies have been implemented on a 2017 GM Chevy Bolt EV. A real time operating system (QNX) has been used and was time-synced with the localization suite in the test vehicle. Closed loop results with accuracies up to 50 cm of lateral error have been achieved using the test vehicle. | en |
| dc.description.abstractgeneral | The research into self-driving cars has been one of the most sought out areas these past couple of decades. There are many components into building a self-driving car - Sensing, Perception, Localization, Navigation. Lot of strategies have been developed over the years with waypoint navigation being the most widely used for navigation an autonomous vehicle. Waypoint Navigation utilizes GPS data to move the car from one point to the other. The traditional process of this strategy involves two parts - curve fitting between waypoints and using a control scheme to track the path with the car. Numerous methods have been developed to fit a curve in between two points. Most of these methods use a variant of 3rd degree or higher order polynomials . Also different control strategies have been developed to track the generated path. Model predictive control strategies are among the popular control architectures used for this purpose. This work proposes a novel method to track a path using clothoids. The proposed algorithm has a novel approach of integrating the path construction and control strategy. The algorithm also has a low computational requirement making it highly suitable for implementation in real-time. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:33321 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/107832 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Autonomous Vehicles | en |
| dc.subject | Navigation | en |
| dc.subject | Clothoids | en |
| dc.subject | Control | en |
| dc.subject | Curve Fitting | en |
| dc.title | Autonomous Vehicle Waypoint Navigation Using Hyper-Clothoids | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Mechanical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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