Self-Balancing Robot For Drink Delivery Yuwei Wang ‘17 and Hung Nguyen ‘17 Department of Engineering Advisor: Prof. H. Blaise Introduction Motivated by the annual RoboWaiter competition held by Trinity College Engineering Department, this project aims to create a device similar to that of an automated drink carrier, but with a major change in design: the robot is self-balanced on two wheels. With a compact size and good maneuverability, this self-balancing robot is intended for narrow aisles on modes of public transportation such as airplanes, trains and buses. The major challenge lies in the design of an effective controller to achieve self-balance while traveling at a desired speed. Control System Design An optimal controller is designed using the Linear Quadratic Regulator method. Sensors including gyroscope, accelerometer and rotary encoders are used for real-time state determination. By passing the states through a feedback gain, the controller computes the required voltage input to the DC motors to achieve self-balance. The controller is both simulated and implemented to hardware in discrete time using Simulink. Finally RC transmitter and receiver are implemented to remotely control backward/forward and steering motion of the robot. Simulation Results Problem Statement Objective: To deliver drinks without spilling by using a self- balancing robot. Specific Goals: To stay self-balanced at all time To be remotely controlled To be resistant to some degree of disturbance Mathematical Model Dynamic equations of the system are derived and linearized to construct a state-space model below in terms of state variables and control input. where x = displacement of the robot km = motor’s torque constant ϴ = tilt angle ke = back EMF constant Mp = mass of the robot’s chassis R = terminal resistance Ip = moment of inertia of the robot’s chassis r = wheel radius l = height of robot’s center of gravity To reduce noise in the accelerometer and to resolve drifting problem with the gyroscope, a complimentary filter is used to obtain the tilt angle of robot LQR method: weight matrices Q and R are determined based on the optimal simulation results, and the feedback gain is computed in MATLAB using Riccati equation K = [-4.43 -24.08 73.53 5.34] Final Design Filtered vs Unfiltered measured angle Simulation of robot tracking desired states Major Components DC motor& encoder MPU6050 IMU sensor Arduino Mega2560 Arduino Mega2560 L298N motor driver RC transmitter/ receiver Reference & Acknowledgement Reference 1. David L. Kleinman, P. Krishna Rao, Continuous-Discrete Gain Transformation Methods for Linear Feedback Control, 1977 2. Yeonhoon Kim, Soo Hyun Kim and Yoon Keun Kwak, Dynamic Analysis of a Non-holonomic Two-Wheeled Inverted Pendulum, Journal of Intelligent and Robotic Systems, 2005 Acknowledgement Professor Harry Blaise Professor John Mertens Andrew Musulin Simulink model for implementation Prototype of the robot

Self-Balancing Robot For Drink Delivery · MPU6050 IMU sensor Arduino Mega2560 Arduino Mega2560 L298N motor driver RC transmitter/ receiver Reference & Acknowledgement Reference 1

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Page 1: Self-Balancing Robot For Drink Delivery · MPU6050 IMU sensor Arduino Mega2560 Arduino Mega2560 L298N motor driver RC transmitter/ receiver Reference & Acknowledgement Reference 1

Self-Balancing Robot For Drink DeliveryYuwei Wang ‘17 and Hung Nguyen ‘17

Department of EngineeringAdvisor: Prof. H. Blaise

IntroductionMotivated by the annual RoboWaiter competition held by Trinity College Engineering Department, this project aims to create a device similar to that of an automated drink carrier, but with a major change in design: the robot is self-balanced on two wheels. With a compact size and good maneuverability, this self-balancing robot is intended for narrow aisles on modes of public transportation such as airplanes, trains and buses. The major challenge lies in the design of an effective controller to achieve self-balance while traveling at a desired speed.

Control System DesignAn optimal controller is designed using the Linear Quadratic Regulator method. Sensors including gyroscope, accelerometer and rotary encoders are used for real-time state determination. By passing the states through a feedback gain, the controller computes the required voltage input to the DC motors to achieve self-balance. The controller is both simulated and implemented to hardware in discrete time using Simulink. Finally RC transmitter and receiver are implemented to remotely control backward/forward and steering motion of the robot.

Simulation Results

Problem StatementObjective: To deliver drinks without spilling by using a self-balancing robot.

Specific Goals:To stay self-balanced at all timeTo be remotely controlledTo be resistant to some degree of disturbance

Mathematical ModelDynamic equations of the system are derived and linearized to construct a state-space model below in terms of state variables and control input.

where

x = displacement of the robot km = motor’s torque constant

ϴ = tilt angle ke = back EMF constant

Mp = mass of the robot’s chassis R = terminal resistance

Ip = moment of inertia of the robot’s chassis r = wheel radius

l = height of robot’s center of gravity

To reduce noise in the accelerometer and to resolve drifting problem with the gyroscope, a complimentary filter is used to obtain the tilt angle of robot

LQR method: weight matrices Q and R are determined based on the optimal simulation results, and the feedback gain is computed in MATLAB using Riccati equation

K = [-4.43 -24.08 73.53 5.34]

Final Design

Filtered vs Unfiltered measured angle

Simulation of robot tracking desired states

Major Components

DC motor&encoder

MPU6050IMU sensor

Arduino Mega2560

L298Nmotor driver

RC transmitter/receiver

Reference & AcknowledgementReference1. David L. Kleinman, P. Krishna Rao, Continuous-Discrete Gain Transformation Methods for Linear Feedback Control, 1977

2. Yeonhoon Kim, Soo Hyun Kim and Yoon Keun Kwak, Dynamic Analysis of a Non-holonomic Two-Wheeled Inverted Pendulum, Journal of Intelligent and Robotic Systems, 2005

AcknowledgementProfessor Harry BlaiseProfessor John MertensAndrew Musulin

Simulink model for implementation Prototype of the robot