GPS Guides Robotic Car

Arduino UNO in Action

In this project article, Raul builds a robotic car that navigates to a series of GPS waypoints. Using the Arduino UNO for a controller, the design is aimed at robotics beginners that want to step things up a notch. In the article, Raul discusses the math, programming and electronics hardware choices that went into this project design.

By Raul Alvarez-Torrico

In this article I lay out a basic differential drive robotic car for waypoint autonomous navigation using the Global Positioning System (GPS). The robotic car receives a list of GPS coordinates, and navigates to waypoints in their given order. To understand how it works, I will discuss concepts about GPS, a simple approach to implement autonomous navigation using GPS, the hardware required for the task, how to calculate navigation vectors using the “Haversine Formula” and the “Forward Azimuth Formula” and a simple implementation of a moving average filter for filtering the GPS coordinate readings. I also discuss a simple approach to navigation control by minimizing the robotic car’s distance and heading error with respect to the goal.

This project is aimed at beginners with basic robotic car experience—that is, line followers, ultrasonic obstacle avoiders and others who now want to try something a little more complex—or anyone who is interested in the subject.

Figure 1 shows the main components of the system. The GPS receiver helps to calculate the distance from the robotic car to the goal. With the aid of a digital compass, the GPS also helps to determine in which direction the goal is located. Those two parameters—distance and direction—give us the navigation vector required to control the robotic car toward the goal. I used a four-wheel differential drive configuration for the car, which behaves almost the same as a two-wheel differential drive. The code provided with the project should work well with both configurations.

Figure 1
GPS Robotic Car block diagram

To calculate the distance to the goal, I used the Haversine Formula, which gives great-circle distances between two points on a sphere from their longitudes and latitudes. The Forward Azimuth Formula was used to calculate the direction or heading. This formula is for the initial bearing which, if followed in a straight line along a great-circle arc, will take you from the start point to the end point. Both parameters can be calculated using the following known data: The goal’s GPS coordinate, the robotic car’s coordinate obtained from the GPS receiver and the car’s heading with respect to North obtained from the digital compass.

The robotic car constantly recalculates the navigation vector and uses the obtained distance and heading to control the motors to approach the goal. I also put a buzzer in the robotic car to give audible feedback when the robotic car reaches the waypoints.

HARDWARE

As shown in Figure 1, I used an Arduino UNO board as the main controller. I chose Arduino because it’s incredibly intuitive for beginners, and it has an enormous constellation of libraries. The libraries make it easy to pull off reasonably advanced projects, without excessive details about the hardware and software drivers for sensors and actuators.

The GPS receiver I chose for the task is the HiLetgo GY-GPS6MV2 module, based on the U-blox NEO-6M chip. The digital compass is the GY-271 module, based on the Honeywell HMC5883L chip. Both are low-cost and ubiquitous with readily available Arduino libraries. The U-blox NEO-6M has a UART serial communication interface, and the HMC5883L works with the I2C serial protocol. To avoid interference, the compass should be placed at least 15 cm above the rest of the electronics.

The DC motors are driven using the very popular L298N module, based on the STMicroelectronics L298N dual, full-bridge driver. It can drive two DC motors with a max current of 2 A per channel. It can also drive two DC motors in each channel if the max current specification is not surpassed—which is what I’m doing with the four-wheel drive chassis I used for my prototype. The chassis has a 30 cm × 20 cm aluminum platform, four generic 12 V DC 85 rpm motors and wheels that are 13 cm in diameter. But almost any generic two-wheel or four-wheel drive chassis can be used.

Figure 2
Circuit diagram for the Robotic Car project

For supplying power to the robotic car, I used an 11.1 V, 2,200 mA-hour (LiPo) Lithium-Polymer battery with a discharge rate of 25C. For my type of chassis, a battery half that size should also work fine. Figure 2 shows the circuit diagram for this project, and Figure 3 shows the finished car.

Figure 3
Completed GPS Robotic Car

GLOBAL POSITIONING SYSTEM

The Global Positioning System (GPS) is a global navigation satellite system owned by the United States government. It provides geolocation and time information to any GPS receiver on the surface of the Earth, whenever it has unobstructed line of sight to at least four GPS satellites—the more the better [1]. GPS receivers typically can provide latitude and longitude coordinates with an accuracy of about 2.5 m to 5 m under ideal conditions, such as good sky visibility and lots of visible satellites. My robotic car is programmed with one or more waypoints given by latitude and longitude coordinates, and the car’s GPS receiver gives its actual position in the same type of coordinates.  …

Read the full article in the June 347 issue of Circuit Cellar
(Full article word count: 3773 words; Figure count: 8 Figures.)

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NVIDIA Graphics Tapped for Mercedes-Benz MBUX AI Cockpit

At the CES show last month, Mercedes-Benz its NVIDIA-powered MBUX infotainment system–a next-gen car cabin experience can learn and adapt to driver and passenger preferences, thanks to artificial intelligence.

According to NVIDIA, all the key MBUX systems are built together with NVIDIA, and they’re all powered by NVIDIA. The announcement comes a year after Huang joined Mercedes-Benz execs on stage at CES 2017 and said that their companies were collaborating on an AI car that would be ready in 2018.

Powered by NVIDIA graphics and deep learning technologies, the Mercedes-Benz User Experience, or MBUX, has been designed to deliver beautiful new 3D touch-screen displays. It can be controlled with a new voice-activated assistant that can be summoned with the phrase “Hey, Mercedes. It’s an intelligent learning system that adapts to the requirements of customers, remembering such details as the seat and steering wheel settings, lights and other comfort features.

The MBUX announcement highlights the importance of AI to next-generation infotainment systems inside the car, even as automakers are racing put AI to work to help vehicles navigate the world around them autonomously. The new infotainment system aims to use AI to adapt itself to drivers and passengers— automatically suggesting your favorite music for your drive home, or offering directions to a favorite restaurant at dinner time. It’s also one that will benefit from “over-the-air” updates delivering new features and capabilities.

Debuting in this month (February) in the new Mercedes-Benz A-Class, MBUX will power dramatic wide-screen displays that provide navigation, infotainment and other capabilities, touch-control buttons on the car’s steering wheel, as well as an intelligent assistant that can be summoned with a voice command. It’s an interface that can change its look to reflect the driver’s mood—whether they’re seeking serenity or excitement—and understand the way a user talks.

NVIDIA | www.nvidia.com

Small, Self-Contained GNSS Receiver

TM Series GNSS modules are self-contained, high-performance global navigation satellite system (GNSS) receivers designed for navigation, asset tracking, and positioning applications. Based on the MediaTek chipset, the receivers can simultaneously acquire and track several satellite constellations, including the US GPS, Europe’s GALILEO, Russia’s GLONASS, and Japan’s QZSS.

LinxThe 10-mm × 10-mm receivers are capable of better than 2.5-m position accuracy. Hybrid ephemeris prediction can be used to achieve less than 15-s cold start times. The receiver can operate down to 3 V and has a 20-mA low tracking current. To save power, the TM Series GNSS modules have built-in receiver duty cycling that can be configured to periodically turn off. This feature, combined with the module’s low power consumption, helps maximize battery life in battery-powered systems.

The receiver modules are easy to integrate, since they don’t require software setup or configuration to power up and output position data. The TM Series GNSS receivers use a standard UART serial interface to send and receive NMEA messages in ASCII format. A serial command set can be used to configure optional features. Using a USB or RS-232 converter chip, the modules’ UART can be directly connected to a microcontroller or a PC’s UART.

The GPS Master Development System connects a TM Series Evaluation Module to a prototyping board with a color display that shows coordinates, a speedometer, and a compass for mobile evaluation. A USB interface enables simple viewing of satellite data and Internet mapping and custom software application development.
Contact Linx Technologies for pricing.

Linx Technologies
www.linxtechnologies.com