SIPHER students: SIPHER students: Jessica Kane and Thao Jessica Kane and Thao NguyenNguyen

Graduate Student Graduate Student Advisors:Advisors: Graham Graham Hemingway, Peter Hemingway, Peter HumkeHumke

Model-Based Autonomous Car Model-Based Autonomous Car Controller DesignController Design

The VECPAV computing platform has been constructed to allow for system design in Simulink during design time and for automatic C-code generation and distribution onto real-time QNX computational nodes.

Closed-Feedback Hardware Loop:•Car sends its (x, y, z-rotation) position to tracker •Tracker transmits information to “the Boxx” processor•Data is processed through the controller•Steering and throttle signals are transmitted to real-time nodes•Radio transmitter receives the signals and sends them to the car

AbstractA small radio-controlled car was equipped with sensors enabling it to localize itself. Using the Vanderbilt Embedded Computing Platform for Autonomous Vehicles (VECPAV)—an existing infrastructure designed for autonomous helicopter flight—a PD-controller was developed to induce the car to autonomously travel a given set of trajectories.

MotivationAutonomously driven vehicles are no longer a dream for the future, but a reality in the present. Researchers have successfully developed self-driven, full-size automobiles. Model-based experimentation with controllers for an autonomous remote-controlled vehicle continues this research on a smaller scale.

ResultsThe model-based controller enabled the car to follow various trajectories autonomously. In the following figures, the green line is the desired trajectory; the blue line is the car’s actual position.

Future•Incorporate second car and/or helicopters•Develop more accurate model of the car for error calculations•Design more robust, adaptive speed controller

Figure A: Tracker Coordinate System

The room’s coordinate system includes a transition point from +180° to -180°. It caused discontinuity in the heading error and inconsistent behavior in the car’s motion. The final solution renormalizes heading error (4) from -90° to +90°.

Real-time Embedded Software System:The Simulink model run by RT-Lab is composed of a loop between the console and the controller.•In the console

−Trajectory outline− Scopes for viewing real-time data−Constants to be adjusted in real-time

•In the controller−Data received from the console−Errors calculated:

Approach

The throttle controller (6) is a simple P-controller. The steering controller (8) is a PD-controller for higher accuracy.

Figure B: Straight Line TrajectoryThe ”stepping” characteristic is caused by the car’s motor. Its slowest speed is faster than the trajectory. The car compensates by stopping before it gets ahead of the trajectory. Motion resumes when the car has fallen behind the trajectory.

Figure C: Counter-clockwise Circle TrajectoryHere, the car completes eight complete counter-clockwise circles. The car remains consistently behind the trajectory because the speed controller is designed to move the car only when it is more than 200 mm from its desired position.

Figure D: “Racetrack” TrajectoryDesigned to determine how the controller handles combinations of straight-line and circular motion.

Figure E: Figure-8 TrajectoryDesigned to test the controller’s response to a combination of diagonal lines, clockwise semicircles, and counterclockwise semicircles.

”r”—reference (trajectory) coordinates ”m”– measured (tracker) coordinate