Student Author Information

Nicholas J. Savino, Lynchburg CollegeFollow

Access Type

Open Access

Presentation Type

Oral Presentation

Department

Physics

Abstract

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as various biases and various initial separation distances on the time it takes the simulated rescue craft to reach the target. The simulations suggested that it is most efficient to continually correct the direction of the simulated rescue craft for movement of the target when the object is moving at random. It is thought that these simulations can model not only the small scale watercraft, but also full size boats. Self-driving technology used here can be applicable in search and rescue missions where conditions may be too harsh for human controlled watercraft an impractical for remote controlled watercraft. This experiment also raises new questions in methods of control that can utilize machine learning, to detect patterns of a moving target.

Faculty Mentor(s)

Dr. William Roach, Dr. Nancy Cowden, Dr. Crystal Moorman

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Autonomous Watercraft Simulation and Programming

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as various biases and various initial separation distances on the time it takes the simulated rescue craft to reach the target. The simulations suggested that it is most efficient to continually correct the direction of the simulated rescue craft for movement of the target when the object is moving at random. It is thought that these simulations can model not only the small scale watercraft, but also full size boats. Self-driving technology used here can be applicable in search and rescue missions where conditions may be too harsh for human controlled watercraft an impractical for remote controlled watercraft. This experiment also raises new questions in methods of control that can utilize machine learning, to detect patterns of a moving target.