Difference between revisions of "F17: Vindicators100"

Revision as of 22:58, 22 December 2017

Grading Criteria

Abstract

Objectives & Introduction

Show list of your objectives. This section includes the high level details of your project. You can write about the various sensors or peripherals you used to get your project completed.

Team Members & Responsibilities

• Sameer Azer
  • Project Lead
  • Sensors
  • Quality Assurance
• Kevin Server
  • Unit Testing Lead
  • Control Unit
• Delwin Lei
  • Sensors
  • Control Unit
• Harmander Sihra
  • Sensors
• Mina Yi
  • DEV/GIT Lead
  • Drive System
• Elizabeth Nguyen
  • Drive System
• Matthew Chew
  • App
  • GPS/Compass
• Mikko Bayabo
  • App
  • GPS/Compass
• Rolando Javier
  • App
  • GPS/Compass

Schedule

Show a simple table or figures that show your scheduled as planned before you started working on the project. Then in another table column, write down the actual schedule so that readers can see the planned vs. actual goals. The point of the schedule is for readers to assess how to pace themselves if they are doing a similar project.

Parts List & Cost

Design & Implementation

Hardware Design

In this section, we provide details on hardware design for each component - power management, drive, sensors, app, and GPS.

Power Management

Block Diagram for whole power management system.

We used an EV-VN7010AJ Power Management Board to monitor real-time battery voltage. The power board has a pin which outputs a current proportional to the battery voltage. Connecting a load resistor between this pin and ground gives a smaller voltage proportional to the battery voltage. We read this smaller voltage using the ADC pin on the SJOne Board.

From battery voltage, it is split up into two 5V rails, an analog 5V (for the servos) and a digital 5V (for boards, transceivers, LIDAR, US sensors, etc.). We're using two buck converters to step the voltage down from battery voltage. There is also a battery voltage rail which goes to the drive system, which they then PWM to get their desired voltage level.

There is also an LM7805 regulator which is used just to power some of the power management chip's control signals.

Drive

Our drive system contains the following hardware components: Motor driver Propulsion motor 5050 LED modules Steering servo LCD Display CAN transceiver The motor driver is powered by our battery (about 16v). It takes in a PWM signal from the SJOne board. It also has a control signal, SLP, which is active low and it is tied high from the 3.3v power rail. The motor driver outputs two signals OUTA and OUTB used to control the propulsion motor which outputs signals for CH A and CH B to the SJOne board. It is also powered by our 16v battery. The steering servo is controlled by the SJOne board via PWM and requires a 5v input. The headlights use a MOSFET where the drain is connected to the 16v battery. The gate is a PWM signal input from the SJOne board which outputs at source, to the LED module's Vin. The LCD display requires a 5v input and has two control signals, EN and RS. It receives inputs from the SJOne board via GPIO for D0-D3 pins to display items on the LCD screen. Lastly, as per all other systems, all communication between them is done via CAN and so drive also has a CAN transceiver which is powered with 3.3v connected to the SJOne's RX and TX line on pin P0.0 and pin P0.1

Diagram of Drive System

Sensors

We are using two sets of sensors - a LIDAR and 4 ultrasonic sensors.The Sensors System Hardware Design provides an overview of the entire sensors system and its connections.

Sensors System Hardware Design

LIDAR

LIDAR System Diagram

Our LIDAR system has two main separate hardware components - the LIDAR and the servo. The LIDAR is mounted on the servo to provide a "big-picture" view of the car's surroundings. The LIDAR System Diagram shows the pin connections between the LIDAR and servo to the SJOne board. The bottom portion in red is our servo and mounted on top is the LIDAR. Both the LIDAR and servo require a 5v input. However, to minimize noise, the servo is powered via the 5v analog power rail and the lidar is powered via the 5v digital power rail. The LIDAR is connected to the SJOne board using the i2c2 pins (P0.10 & P0.11). As per the datasheet's instructions, a 680 uF capacitor was added across the Vcc and GND lines. The servo is driven using the PWM1 pin which is P2.0 on the SJOne board. A level shifter between the servo and the SJOne board was necessary since the SJOne board outputs a 3.3v signal, but the servo works with a 5v signal. To mount the LIDAR onto the servo, a case that holds the LIDAR and an actual mounting piece was designed and printed using a 3D printer. The Lidar System and Mount Diagrams shows the three components for mounting the lidar to the servo and then to the car.

LIDAR Mount Diagrams Left: Case for the LIDAR Middle: Connector for the LIDAR and servo Right: Servo to car interface to keep the LIDAR steady

Ultrasonic Sensors

Ultrasonic Sensor System

CAD Design for Ultrasonic Sensor Mount The ultrasonic sensor setup that we have installed on the autonomous car is three ultrasonic sensors on the front and one on the back. The layout of the front sensors is one sensor down the middle and one on each side angled at 26.4/45 degrees. The sensors are powered using the 5v digital power rail. In addition to the sensors, an Analog/Digital mux was required to allow us to obtain values for all 4 sensors with the use of one ADC pin from the SJOne board. All sensors are daisy chained together with the initial sensor triggered by a GPIO pin, P2.5 on the SJOne board. The first sensor outputs a PWM signal shortly after the trigger. The PWM signal is connected to the trigger on the next sensor and so forth. In turn, this triggers the second sensor and continuously triggers the sensors throughout the chain. Chaining the sensors was necessary to ensure that there is no cross-talk between the sensors. By configuring our hardware components in this manner, after about 10ms the first sensor will take measurements. Once the measurement is complete, the next sensor will take its measurement and so forth. Essentially, only one sensor is active at any given time. A mount for all the ultrasonic sensors was designed and 3D printed. In the CAD Design for Ultrasonic Sensor Mount the top right component is used for the rear sensor while the remaining three are combined to create the front holding mount that is attached to the front of our car.

App

ESP8266 Interface with SJOne Board Our application uses an ESP8266 as our bridge for wifi connectivity. The ESP8266 takes an input voltage of 5v which is connected to the 5v digital power rail. The ESP8266's RX is connected to the TXD2 pin on the SJOne board and the TX is connected to the RXD2 on the SJOne board for UART communication. As with all other components, we are using CAN for communication between the boards and so it uses P0.0 and P0.1 which is connected to the CAN transceiver's RX and TX pins respectively.

GPS

Compass and GPS Interface with SJOne Board

Our GPS system uses the following: uBlox Neo M8N for GPS LSM303 Mangetometer for our compass The uBlock Neo uses UART for communication so the TX is connected to the SJOne's RXD2 pin and the RX is connected to the TXD2 pin. The module is powered directly from the SJOne board using the onboard 3.3v output pin which has been connected to a 3.3v power rail to supply power to other modules. The LSM303 magnetometer module uses i2c for communication. The SDA is connected to the SJOne's SDA2 pin and the SCL is connected to the SJOne's SCL2 pin. The LSM303 also needs 3.3v to be powered and it is also connected to the 3.3v power rail.

Left: CAD of our tower that holds the platform Right: Platform to place our nav components on

PCB Design

Once we finalized all the separate systems, we were able to design our PCB. During this process, we collected all of the circuit diagrams and the necessary pinouts from each group to decide where to place each of the components. In addition, we added extra pins where necessary in order to test each component easily. For example, we provided two set of SJOne pin connections - one for the ribbon cable that connects to the SJOne board and one extra set of pins that provide us access to the same SJOne pins. The reasoning for this was that it would provide us access to the pins to connect different tools to aid in debugging.

Image of our PCB trace

Hardware Interface

In this section we provide details on the hardware interfaces used for each system.

Power Management

The EV-VN7010AJ has a very basic control control mechanism. It has an output enable pin, MultiSense enable pin, and MultiSense multiplexer select lines. All of the necessary pins are either permanently tied to +5V, GND, or a GPIO pin on the SJOne Board. The MultiSense pin outputs information about the current battery voltage. The MultiSense pin is simply connected to an ADC pin on the SJOne board.

Drive

The drive system is comprised of two main parts - the motor driver and propulsion motor and the steering servo. We provide information about each of these component's hardware interfaces.

Motor Driver & Propulsion Motor

The motor driver is used to control the propulsion motor via OUTA and OUTB. It is driven by the SJOne board using a PWM signal connected to pin P2.0. The PWM frequency is set to 20,000 kHz.

Steering Servo

Our steering servo is driven via a PWM signal as well. The PWM frequency is set to 50 Hz. However, since we are already using a PWM signal for the motor, we used a repetitive interrupt timer (RIT) to manually generate a PWM signal using a GPIO pin for the steering servo. The RIT had to be modified to allow us to count in microseconds (uS) since the original only allows to count in ms. To do this, we added an additional parameter uint32_t time. These time values are using to calculate our compare value which tells us how often to trigger the callback function. Since we wanted our function to trigger every 50 us, we essentially need to calculate how many CPU clock edges we need before the callback is triggered.

For example, we wanted to trigger our callback every 50 us. Thus, our prefix is 1000000 (amount of us in 1 second) and our time is 50 (us). This will give us the number of 50 us in 1 second in terms of us, which is 20000. When we take our system clock (48 MHz) and divide it by the number we just calculated and it gives us the number of CPU cycles we want before the callback is triggered.

// function prototype
void rit_enable(void_func_t function, uint32_t time, uint32_t prefix);
// Enable RIT - function, time, prefix
rit_enable(RIT_PWM, RIT_INCREMENT_US, MICROSECONDS);
// modified calculation 
LPC_RIT->RICOMPVAL = sys_get_cpu_clock() / (prefix / time);

We used pin P1.19 as a GPIO output pin to generate our PWM signal. In our RIT callback, it brings the GPIO pin high or low. The algorithm to generate the signal is as follows:

1. Initialize a static counter to 0
2. Increment by 50 on each callback (we chose 50 us so as not to eat up too much CPU time)
3. Check if counter is greater than or equal to our period (50 Hz)
   1. Assert GPIO pin high
   2. Reset count
4. Check if counter is greater than or equal to our target
   1. Bring GPIO pin low

NOTE Our target has to be in terms of us since all of our calculations are in us. We get the target for our RIT by obtaining a target angle from control. We use a mapping function to generate our target in us from the desired angle. The initial value for the mapping function calculation was done by using a function generator with our steering servo.

• We found the min and max values in terms of ms and converted it to us.
• We then used these min and max values to move the wheels and mark it at each extreme on a sheet of paper.
• Using the marks, we calculated the angle and used the midpoint of the angle as 0°. That means that the minimum is a negative angle value and maximum is a positive angle value.

We were then able to generate a simple mapping function to map angles in degrees to us using: (uint32_t) (((angle - MIN_STEERING_DEGREES) * OUTPUT_RANGE / INPUT_RANGE) + MIN_MICRO_SEC_STEERING);

LCD Screen

The LCD screen was interfaced with the SJOne board using a variety of GPIO pins for data (D0, D1, D2, D3) and to enable the screen (EN).

Headlights

Our driver for the headlights uses pin P2.5 to generate a PWM signal to turn our headlights on or off. We are using the same frequency as our motor driver (20,000 kHz) since we can only have one PWM frequency for all PWM pins on our SJOne board.

Sensors

Ultrasonic Sensor Interface

There are two mounting brackets for our ultrasonic sensors; one angled at 26.4 degrees and another at 45 degrees. The mounting brace on the front bumper of the autonomous car allows us to install either brackets based on our desired functionality. The ultrasonic sensors are also interfaced with an ADC mux because the SJOne board does not have enough ADC pins to process our sensors individually.

LIDAR and Servo Interface

The LIDAR communicates via i2c. Typically, when executing a read operation, there is a write and then a read. It writes the device address and provides a repeat start. However, the LIDAR expects a stop signal and does not respond to a repeat start, as shown in the LIDAR Read Timing Diagram.

LIDAR Read Timing Diagram

Thus, we utilized a modified version of the i2c driver that does two completely separate write and read operations. This provides the proper format expected by the lidar.

The LIDAR driver is as follows:

• Begin with initialization

lidar_i2c2 = &I2C2::getInstance();
lidar_i2c2->writeReg((DEVICE_ADDR << 1), ACQ_COMMAND, 0x00);
lidar_servo = new PWM(PWM::pwm1, SERVO_FREQUENCY);

Initialize lidar to default configuration

    // set configuration to default for balanced performance
    lidar_i2c2->writeReg((DEVICE_ADDR << 1), 0x02, 0x80);
    lidar_i2c2->writeReg((DEVICE_ADDR << 1), 0x04, 0x08);
    lidar_i2c2->writeReg((DEVICE_ADDR << 1), 0x1c, 0x00);

• Write to reg via i2c 0x04 to 0x00 to indicate a measurement is to be taken
• Check the status register 0x01 until the least significant bit is set
• Send the register value 0x8f where the measurement is stored
• Read the value sent back from the lidar and store it in an array
• Shift the contents of index 0 to the left 1 byte and or it with the contents of the array in index 1

return (bytes[0] << 8) | bytes[1];

The driver for the servo simply uses the PWM functions from the library provided to us to initialize the PWM signal to our desired frequency. In our case, we used a 50Hz signal. We also used the set(dutyCycle) function to set the PWM pin to output the desired pattern based on the duty cycles.

GPS

The GPS (uBlox NEO M8N) uses UART to send GPS information. We've disabled all messages except for $GNGLL, which gives us the current coordinates of our car. The GPS then sends parse-able lines of text over its TX pin that contains the latitude, longitude, and UTC. The SJOne board receives this text via RXD2 (Uart2). The latitude and longitude were given in degrees minutes in the format of dddmm.mmmmmm where d is the degrees and m is the minutes. To convert it to just degrees, the number was divided by one hundred and then the decimal portion was separated from it, divided by 60 to acquire the degrees equivalent, and then added back to the initial degrees of the number.

The GPS can be configured by sending configuration values over its RX pin. To do this, we send configurations over the SJOne's TXD2 pin and save the configuration by sending a save command to the GPS.

Compass

We made a compass using the LSM303 Magnetometer. To do this, we first had to configure the mode register to allow for continuous reads. We also had to set the gain by modifying the second configuration register. The compass can then be polled at 10Hz to read the X, Y, and Z registers, which are comprised of 6 1-byte wide registers. From this, we can calculate the heading by atan2(Y,X). We chose to completely neglect the Z axis as we found that it made very little difference for our project.

Software Design

In this section we provide details on our software design for each of the systems. We also delve into our DBC file and the messages sent via CAN bus.

CAN Communication

243.dbc

VERSION ""

NS_ :
    BA_
    BA_DEF_
    BA_DEF_DEF_
    BA_DEF_DEF_REL_
    BA_DEF_REL_
    BA_DEF_SGTYPE_
    BA_REL_
    BA_SGTYPE_
    BO_TX_BU_
    BU_BO_REL_
    BU_EV_REL_
    BU_SG_REL_
    CAT_
    CAT_DEF_
    CM_
    ENVVAR_DATA_
    EV_DATA_
    FILTER
    NS_DESC_
    SGTYPE_
    SGTYPE_VAL_
    SG_MUL_VAL_
    SIGTYPE_VALTYPE_
    SIG_GROUP_
    SIG_TYPE_REF_
    SIG_VALTYPE_
    VAL_
    VAL_TABLE_

BS_:

BU_: DBG SENSORS CONTROL_UNIT DRIVE APP NAV

BO_ 100 COMMAND: 1 DBG
 SG_ ENABLE : 0|1@1+ (1,0) [0|1] "" DBG

BO_ 200 ULTRASONIC_SENSORS: 6 SENSORS
 SG_ front_sensor1 : 0|12@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP,DRIVE
 SG_ front_sensor2 : 12|12@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP,DRIVE
 SG_ front_sensor3 : 24|12@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP,DRIVE
 SG_ back_sensor1 : 36|12@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP,DRIVE

BO_ 101 LIDAR_DATA1: 7 SENSORS
 SG_ lidar_data1 : 0|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar_data2 : 11|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar_data3 : 22|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar_data4 : 33|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar_data5 : 44|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP

BO_ 102 LIDAR_DATA2: 6 SENSORS
 SG_ lidar2_data6 : 0|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar2_data7 : 11|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar2_data8 : 22|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP
 SG_ lidar2_data9 : 33|11@1+ (1,0) [0|0] "cm" CONTROL_UNIT,APP

BO_ 123 DRIVE_CMD: 3 CONTROL_UNIT
 SG_ steer_angle : 0|9@1- (1,0) [-23|23] "degrees" DRIVE,APP
 SG_ speed : 9|6@1+ (1,0) [0|20] "mph" DRIVE,APP
 SG_ direction : 15|1@1+ (1,0) [0|1] "" DRIVE,APP
 SG_ headlights : 16|1@1+ (1,0) [0|1] "" DRIVE,APP

BO_ 122 SENSOR_CMD: 1 CONTROL_UNIT
 SG_ lidar_zero : 0|8@1- (1,0) [-90|90] "degrees" SENSORS,APP

BO_ 121 GPS_POS: 8 NAV
 SG_ latitude : 0|32@1+ (0.000001,-90) [-90|90] "degrees" APP
 SG_ longitude : 32|32@1+ (0.000001,-180) [-180|180] "degrees" APP

BO_ 120 GPS_STOP: 1 NAV
 SG_ stop : 0|1@1+ (1,0) [0|1] "" APP,CONTROL_UNIT

BO_ 119 GPS_STOP_ACK: 1 CONTROL_UNIT
 SG_ stop_ack : 0|1@1+ (1,0) [0|1] "" APP,NAV

BO_ 148 LIGHT_SENSOR_VALUE: 1 SENSORS
 SG_ light_value : 0|8@1+ (1,0) [0|100] "%" CONTROL_UNIT,APP

BO_ 146 GPS_HEADING: 3 NAV
 SG_ current : 0|9@1+ (1,0) [0|359] "degrees" APP,CONTROL_UNIT
 SG_ projected : 10|9@1+ (1,0) [0|359] "degrees" APP,CONTROL_UNIT

BO_ 124 DRIVE_FEEDBACK: 1 DRIVE
 SG_ velocity : 0|6@1+ (0.1,0) [0|0] "mph" CONTROL_UNIT,APP
 SG_ direction : 6|1@1+ (1,0) [0|1] "" CONTROL_UNIT,APP

BO_ 243 APP_DESTINATION: 8 APP
 SG_ latitude : 0|32@1+ (0.000001,-90) [-90|90] "degrees" NAV
 SG_ longitude : 32|32@1+ (0.000001,-180) [-180|180] "degrees" NAV

BO_ 244 APP_DEST_ACK: 1 NAV
 SG_ acked : 0|1@1+ (1,0) [0|1] "" APP

BO_ 245 APP_WAYPOINT: 2 APP
 SG_ index : 0|7@1+ (1,0) [0|0] "" NAV
 SG_ node : 7|7@1+ (1,0) [0|0] "" NAV

BO_ 246 APP_WAYPOINT_ACK: 1 NAV
 SG_ index : 0|7@1+ (1,0) [0|0] "" APP
 SG_ acked : 7|1@1+ (1,0) [0|1] "" APP

BO_ 0 APP_SIG_STOP: 1 APP
 SG_ APP_SAYS_stop : 0|8@1+ (1,0) [0|1] "" CONTROL_UNIT

BO_ 2 APP_SIG_START: 1 APP
 SG_ APP_SAYS_start : 0|8@1+ (1,0) [0|1] "" CONTROL_UNIT

BO_ 1 STOP_SIG_ACK: 1 CONTROL_UNIT
 SG_ control_received_stop : 0|8@1+ (1,0) [0|1] "" APP

BO_ 3 START_SIG_ACK: 1 CONTROL_UNIT
 SG_ control_received_start : 0|8@1+ (1,0) [0|1] "" APP

BO_ 69 BATT_INFO: 2 SENSORS
 SG_ BATT_VOLTAGE : 0|8@1+ (0.1,0) [0.0|25.5] "V" APP,DRIVE
 SG_ BATT_PERCENT : 8|7@1+ (1,0) [0|100] "%" APP,DRIVE

CM_ BU_ DBG "Debugging entity";
CM_ BU_ DRIVE "Drive System";
CM_ BU_ SENSORS "Sensor Suite";
CM_ BU_ APP "Communication to mobile app";
CM_ BU_ CONTROL_UNIT "Central command board";
CM_ BU_ NAV "GPS and compass";

BA_DEF_ "BusType" STRING ;
BA_DEF_ BO_ "GenMsgCycleTime" INT 0 0;
BA_DEF_ SG_ "FieldType" STRING ;

BA_DEF_DEF_ "BusType" "CAN";
BA_DEF_DEF_ "FieldType" "";
BA_DEF_DEF_ "GenMsgCycleTime" 0;

BA_ "GenMsgCycleTime" BO_ 256 10;
BA_ "GenMsgCycleTime" BO_ 512 10;
BA_ "GenMsgCycleTime" BO_ 768 500;
BA_ "GenMsgCycleTime" BO_ 1024 100;
BA_ "GenMsgCycleTime" BO_ 1280 1000;

Power Management

The MultiSense pin on the EV-VN7010AJ board outputs real time voltage information. It's connected to an ADC pin on the SJOne Board.The ADC reading is converted in software from a 0-4095 range to the actual voltage.

Drive

The drive software is separated into 3 main sections: period callbacks, drive methods, and LCD library. The period callback file contains the drive controllers, LCD interface, and message handling. The CAN messages are decoded in the 100 Hz task to feed the desired target values into the PID propulsion controller and the servo steering controller. In addition, the control unit CAN message will control the on and off state of the headlights. The drive methods files contain all functions, classes, and defines necessary for the drive controllers. For example, unit conversion factors and unit conversion functions. The LCD library contains the LCD display driver and high-level functions to interface with a 4x20 LCD display in 4-bit mode.

Sensors

Ultrasonic Sensors

For our software design, we set our TRIGGER pin to high to initiate the first ultrasonic to begin sampling. The ultrasonic sensor trigger requires a high for uqat least 20us and needs to be pulled low before the next sampling cycle. We chose to put a vTaskDelay(10) before pulling the trigger pin low. After pulling the pin low, we wait for an additional 50ms for the ultrasonic sensor to finish sampling and output the correct values for the latest reading. Because we have an ADC mux, we have two GPIOs responsible for selecting which ultrasonic sensor we are reading from. Since we have 4 sensors, we cycle between 00 -> 11 and read from the ADC pin P0,26. For filtering, we are doing an average with a sample size of 50.

LIDAR

Our Lidar class contains a reference to the instance of the PWM class (for the servo) and to the I2C2 class (for communication with the lidar). It also contains an array with 9 elements to hold each of the measurements obtained during the 90° sweep.

During development, we found that the LIDAR takes measurements far faster than the servo can turn. Thus, we had to add a delay long enough for the servo to position itself before obtaining readings. To accomplish this, we created a separate task for the LIDAR itself called LidarTask so as not overrun the periodic tasks. This is because we have the ultrasonic sensors and battery information running in the task that would have been most suitable for the lidar. The delay value was obtained from the servo's datasheet that indicated how many degrees it can turn in a specific amount of time.

Lidar Position Diagram

The basic process for the LIDAR is that it sweeps across a 90° angle right in front of it, as shown in the LIDAR Position Diagram. We have a set list of 9 duty cycles for each position we wish to obtain values for. The duty cycles were obtained by calculating the time in ms needed to move 10°. This is covered in further detail in the Implementation section. The basic algorithm is as follows:

1. Iterate through the list of positions in ascending order
   1. Set position of servo
   2. Wait long enough for servo to position itself
   3. Take measurement and store it
2. When ascending order is complete, give the semaphore to send lidar data via CAN
3. Set increment flag to false
4. Iterate through the list of positions in descending order
   1. Set position of servo
   2. Wait long enough for servo to position itself
   3. Take measurement and store it
5. When descending order is complete, give the semaphore to send lidar data via CAN
6. Set increment to true

APP BOARD / ESP8266 / Server / Phone Communication

The Phone communicates to the server via a website. The website was created with the Django Web Framework. Commands from the phone are saved in a database that the ESP8266 and APP Board can poll. Here is the basic flow: The APP board sends a command to read a specific URL. For instance, the APP board can send "http://exampleurl.com/commands/read/" to the ESP8266. The ESP8266 will make a GET request to the server to see if there are any commands it needs to execute. If it finds one, it will send that information over UART to the APP board. To send from the APP to the server, the APP board sends a URL to the ESP8266 such as "http://exampleurl.com/nav/update_heading/?current_heading=0&desired_heading=359". The ESP8266 makes a GET request so that the server can update the current and desired headings.

APP Communication Dataflow

Control Unit

The control unit does the majority of its work in the 10Hz periodic task. The 1Hz task is used to check for CAN bus off and to toggle the control unit's heartbeat LED. The 100Hz task is used exclusively for decoding incoming CAN messages and MIA handling.

The 10Hz and 100Hz tasks employ a state machine which are both controlled by the STOP and GO messages from APP. If APP sends a GO signal, the state machine will switch to the "GO" state. If APP sends a STOP signal, the state machine will switch to the "STOP_STATE" state. While in the "STOP_STATE" state, the program will simply wait for a GO signal from app. While in the "GO" state, the 10Hz task will perform the path finding, object avoidance, speed, and headlight logic and the 100Hz task will begin decoding incoming CAN messages.

Path Finding

For path finding, NAV sends CONTROL_UNIT two headings: projected and current. Projected heading is the heading the car needs to be facing in order to be pointed directly at the next waypoint. This was calculated using the coordinates of the cars current destination versus the coordinates of the next waypoint. Current heading is the direction the car is pointing at that moment. These two values are used to determine which direction to turn to and of what severity to turn.

Using the grid of GPS coordinates, a data structure of nodes is created. Each node is aware of its neighbor nodes to the North, East, South, and West. A modified implementation of the Astar algorithm is used to determine the path of grid nodes, given a start node and an end node. Astar is intended for use with equally spaced GPS coordinates. In this case, our grid is made up of arbitrary GPS coordinates that were picked strategically throughout the campus to avoid "noisy" paths and favor paved paths. Because of our custom grid, Astar was modified to do the following:

1. {A} Given a current node, get a list of every neighbor = (n) and every neighbor's neighbor = (nn).
2. {B} Calculate the distance from every (nn) to the end node = (f)
3. {C} Calculate the distance from the current node to every (nn) = (s)
4. {D} Scale (s) down by 30%. [Why? because the cost of a possible node (nn) is the sum of (f) + (s). Scaling (s) down allows the algorithm to pick nodes that are closer to the end node despite how far they maybe from the current node.]
5. {E} Sum (0.3 * s) + (f) = (c)
6. {F} Pick the (nn) with the least (c) = (nnBest)
7. {G} Set current node to the (n), which is a neighbor of (nnBest).
8. Loop back to {A} if current node is not equal to end node.

Object Avoidance

Object avoidance is broken into multiple steps:

1. Simplify ultrasonic readings to whether the sensor is "blocked" or not.
2. Simplify lidar readings to whether the reading is "blocked", "getting close", or "safe".
3. Determine what direction to turn based solely on lidar.
4. Determine what direction to turn based solely on ultrasonics.
5. Determine final steering angle based on path finding and lidar and ultrasonic direction calculations.

Speed

Because the maximum speed of our car is about 2mph, speed modulation is relatively simple and can be simplified into stop, slow, and full speed. When lidar detects something is "getting close", the speed will be "slow". The speed will also be set to slow when the car is performing a turn or reversing. If all ultrasonic sensors are blocked (including the rear sensor), the car will stop.

Headlights

If the light value received from SENSORS is below 10 percent then CONTROL will send a signal to drive to turn on the headlights.

Implementation

Image of our integration in process

We tested each of the components individually before integrating each component. Once each of the systems were verified to be working correctly, we connected one system at a time. We did this to help us identify if there were any systems that caused problems when adding them to the PCB.

PCB Final Design

Drive

Periodic Init

• Initializes CAN as well as sets servo PWM and motor PWM to 0.
  

1Hz Task

• Used to check the state of CAN. If it's off, LED 1 turns off to indicate that bus is off.
  

10Hz Task

• Sets PID controller target and feedback values
• Sets the direction of the motor based on control values
• Converts motor RPM to duty cycle using with the exponential moving average value
• Sends feedback to control (speed and direction)
  

100 Hz Task

• Receives CAN messages from control and sensors. Control provides speed, direction, and steering angle. We use the sensors data to display it on the LCD display.

GPS

App

Periodic Init

• Init UART
• Init CAN

1Hz Task

• Check CAN bus state and reset if bus off

100Hz Task

• Sends start and stop conditions to start or stop car
• Get destination from ESP and send to control
• Receive CAN messages from each unit with data such as sensor values

Control

Sensors

Periodic Init

• Initialize all GPIOs used for ADC mux and CAN.
  

LIDAR Init

• Initialize PWM and i2c for servo and LIDAR respectively.
  

1Hz Task

• Check CAN bus status
• Send light sensor data to control
  

10Hz Task

• Take Sempaphore from LIDAR task and send values back to control
• Get ADC readings from ultrasonics and filter out any value spikes

100Hz Task

• Send ultrasonic values back to control
  

Testing & Technical Challenges

In this section we provide an overview of our testing methods for each component as well as technical challenges we observed throughout the course of this project.

Testing

For unit testing we used cgreen for each of the modules.

Each of the modules was tested independently before any full integration testing.

GPS

GPS was tested by taking the GPS module and walking around campus to map out accuracy and areas of the campus that gave us inaccurate readings.

Drive

To test the basic functionality of drive we began in a multi-part process. Testing for drive was done by testing each component in the following order:

1. Test motor functionality - get the wheels turning
2. Test the steering capability - turn the wheels
3. Combine both and test

We began with testing the drive functionality by supplying the necessary signals to see whether we can get it to run at different speeds. We did this by programming each of the buttons to supply different duty cycles. We used an oscilloscope to verify the duty cycles along with the frequency since we had issues with incorrect frequencies. This is dicussed further in the Technical Challenges section.

Once that was complete, we used an oscilloscope to verify that our GPIO output was supplying the expected PWM signal. We then programmed the onboard push buttons to subtract or add degrees to verify that the wheels would turn as expected.

Finally, we integrated both parts and provided test values from control to ensure that it behaved as expected.

Sensors

Ultrasonic

Overall testing of the ultrasonic sensors was done by pointing the sensors to different objects and checking the output from Hercules to verify that the values were correct. This is where we found that once we added the analog/digital mux, the values we were receiving were not longer accurate. We go over the details in the Technical Challenges section.

LIDAR

To test the LIDAR, a variety of methods was used. We began by taping a piece of paper in front of the LIDAR and measured the distance. We then looked at the output values obtained and printed each value at each position. This helped us identify that the values were offset or sometimes incorrect because the LIDAR was able to take measurements faster than the servo could move.

To fix this, we began with the datasheet for the servo and found that it took 0.16 seconds to move 60°. Using this measurement, we began by adding a 160ms delay between servo moves. Then, by trial and error we found that a 90ms delay would allow us to retain the correct values.

Technical Challenges

Timing issue after adding Analog/Digital mux

Adding an analog/digital mux caused a lot of problems with obtaining accurate values from the ultrasonic sensors. Since we had chained each of the sensors to take measurements one at a time, we had to finetune the timing to work with the mux because the mux requires us to use GPIO pins for the select lines. Getting the timing down was crucial for switching between each of the sensors to provide the signal to the ADC pin to convert.

Single frequency setting for PWM

Using PWM on the SJOne board only allows you to run all PWM pins at the same frequency. For drive, we required two different frequencies - one for the motor and one for the servo. We solved this issue by generating our own PWM signal using a GPIO pin and a RIT (repetitive interrupt timer).

Inaccurate PWM Frequency

When originally working with PWM for drive, we found that the actual frequency that was set was inaccurate. We expected the frequency to be 20kHz, but when we hooked it up to the oscilloscope we found that it was much higher. It was consistently setting the frequency to output about 10 times the expected value. We came to the realization that since we had been declaring the PWM class as a global, the initialization was not being done properly. We are not sure why this is the case, but after we instantiated the PWM class in the periodic init function, the frequency was correct.

Difficulties with Quadrature Counter

We had issues getting the quadrature counter to give us valid numbers. Our solution was to use LPC's built-in counter instead of having an extra external component.

Finding Closest Node from Start Point

The path-finding algorithm requires the car to find the closest node before it can start the route. However, if the closest node is across a building, the car will attempt to drive through buildings to get to that node. We fixed this by adding more nodes such that the closest node will never be across a building.

Conclusion

This project definitely taught us a lot; it has not only taught us new technical skills such as utilizing CAN bus communication, but it has also taught us life skills. We found how difficult a large scale project like this is to implement with so many different groups and how many things can go wrong. This really showed us the importance of time management and testing.

Project Video

You can find the Vindicators100 project video at https://youtu.be/JlotRHnlk_0

Project Source Code

• Vindicators100 GIT LAB

References

Acknowledgement

We would like to thank Professor Preetpal Kang and his ISAs for the guidance they provided us throughout this project.

References Used

APP

Django Web Framework Documentation
Amazon AWS Documentation

Navigation / GPS

LSM303 Datasheet
uBlox NEO M8N Protocol Description
uBlox NEO M8N Datasheet

SJONE

Social Ledge

Appendix

You can list the references you used.