Published February 5, 2024 © MIT

Edge Detecting Bot using XENSIV™ BGT60UTR11AIP radar

Create your own robot car that can detect and avoid edges effortlessly using the Infineon radar demo kit BGT60UTR11AIP.

AdvancedFull instructions provided895

Things used in this project

Hardware components

Infineon BGT60UTR11AIP DEMO

Raspberry Pi 4 Model B

RFbeam radome lens

Robot car kit which includes motors, batteries, and wheels

Motor controller

Power bank

Software apps and online services

Infineon Radar Development Kit

Story

This project involves a robotic car that is outfitted with Infineon's 60 GHz radar sensors, which aid in detecting platform edges. These sensors also serve to demonstrate the capabilities of the newly released BGT60UTR11AIP, such as accurate distance measurement, swift response times, low power consumption, and compact size. These features make it ideally suited for applications like vacuum cleaners and similar devices.

To give a brief overview of the radar device: The BGT60UTR11AIP is equipped with an ultra-wide bandwidth of 5.6 GHz, enabling FMCW (Frequency Modulated Continuous Wave) operations that can achieve extremely high resolution. This device is not only capable of detecting sub-millimeter movements, ensuring highly sensitive presence and motion detection up to a range of 15 meters, but it also facilitates millimeter-precise range measurements and the recognition of 1D gestures.

Project Block Diagram

Block Diagram

Project Setup

The BGT60UTR11AIP boasts a wide Field of View (FoV) with a range of 60°. However, for this particular project, our aim is to measure distances directly beneath the radar, and as such, it becomes necessary to narrow this FoV to avoid false detections. A radome lens is employed to reduce the radar's FoV to approximately 8°. It is mounted onto the radar board as shown here:

The radome lens mounted on radar board

The picture presents a bottom-view image of the robot, highlighting the placement of the radars that are fitted with radome lenses. Notably, the cylindrical elements visible in white within the image are the radome lenses themselves.

1 / 2 • Bottom view of robot

Working principle

The core concept of the system involves continuously measuring the distance from both radars installed on the robot. As the robot nears an edge, the radars will extend over this edge, consequently registering a greater distance (denoted as X2) in comparison to the measurement taken when the robot is on a flat surface (represented as X1).

Working principle

Python script overview

The Python script utilized for this project is an adaptation of the sample code included with the Radar Development Kit (v3.5.1) which is originally intended for measuring static distances. The two radars are interfaced with a Raspberry Pi. This connection is established using a 'connect by UUID' command, which uniquely identifies each radar device and facilitates secure communication.

Before proceeding with the script execution, it is crucial to install the Python wheels that are provided within the Radar Development Kit.Once the radars are connected, they are configured according to the project’s defined metrics. The distances are then measured at a rate of 60 fps. This high frequency of measurement is essential to detect the presence of an edge accurately.

To facilitate the replication or adaptation of this project, the complete code has been attached.

Before we start with the actual script, we define some helper functions to wrap the radar commands.

This function takes a radar frame and calculates the distance peak from it:

def getRadarDistancePeak( frame ):
    frame_data = frame[0]
    antenna_samples = frame_data[i_ant, :, :]
    distance_peak, _ = algo.compute_distance(antenna_samples)
    return distance_peak

And this one lets the robot reverse and turn a bit. We will do this when a table corner is detected:

def reverseAndTurn():
    stopAll()
    goBack(0.5)
    turnClk(0.15)

Connecting the two devices, identified by UUID:

device1 = DeviceFmcw(uuid="00313853-314c-4c35-3135-303039303137")
device2 = DeviceFmcw(uuid="00313853-3959-3434-3034-303331303335")

UUID can be got by using the “DeviceFmcw.get_list()” command.

Configuring the radar chips:

device1.set_acquisition_sequence(sequence)
device2.set_acquisition_sequence(sequence)
algo = DistanceAlgo(chirp, chirp_loop.loop.num_repetitions)
print("Program started")

Loop through getting distances from radar chips and check for table corner:

while(1):
    # Move robot forward
    goFront(0)

    # Get distance data from radar sensor 1
    frame_contents = device1.get_next_frame()
    distance_peak = getRadarDistancePeak( frame_contents )
    print("Distance 1:" + format(distance_peak, "^05.3f") + "m")
    
    # Reverse and turn the robot if table corner is detected
    if(distance_peak > 0.25):
        reverseAndTurn()
        device1.stop_acquisition()
        device2.stop_acquisition()

    # Get distance data from radar sensor 2
    frame_contents = device2.get_next_frame()
    distance_peak = getRadarDistancePeak( frame_contents )
    print("Distance 2:" + format(distance_peak, "^05.3f") + "m")
    
    # Reverse and turn the robot if table corner is detected
    if(distance_peak > 0.25):
        reverseAndTurn()
        device1.stop_acquisition()
        device2.stop_acquisition()

    print("Still Running")

Working Demo

1 / 3 • Working Demo

And with that, you are well-equipped to embark on your exploration of Infineon radar technology.

Should you have any further inquiries or require deeper insights into the operation and applications of radar sensors, do not hesitate to reach out. We encourage you to create a query on the radar sensor forum page.

Happy experimenting, and we look forward to seeing the innovative ways you will utilize Infineon radars in your projects!

Application Code

# ===========================================================================
# Copyright (C) 2021-2022 Infineon Technologies AG
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
#    this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in the
#    documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of its
#    contributors may be used to endorse or promote products derived from
#    this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# ===========================================================================

# Edge detecting bot source code- Infineon Developer Community

import argparse

from ifxradarsdk import get_version_full
from ifxradarsdk.fmcw import DeviceFmcw
from ifxradarsdk.fmcw.types import FmcwSimpleSequenceConfig, FmcwMetrics
from helpers.DistanceAlgo import *

import RPi.GPIO as GPIO
from time import sleep

in1=10              #Define GPIO pins
in2=9
in3=7
in4=8
in5=6
in6=5

GPIO.setmode(GPIO.BCM)      #diable gpio warnings
GPIO.setwarnings(False)

GPIO.setup(in1,GPIO.OUT)        #setup gpio pins
GPIO.setup(in2,GPIO.OUT)
GPIO.setup(in3,GPIO.OUT)
GPIO.setup(in4,GPIO.OUT)
GPIO.setup(in5,GPIO.OUT)
GPIO.setup(in6,GPIO.OUT)

GPIO.output(in1,GPIO.LOW)       #make all pins low after init
GPIO.output(in2,GPIO.LOW)
GPIO.output(in3,GPIO.LOW)
GPIO.output(in4,GPIO.LOW)
GPIO.output(in5,GPIO.LOW)
GPIO.output(in6,GPIO.LOW)
# -------------------------------------------------
#Motor control functions
def stopAll():
    GPIO.output(in1,GPIO.LOW)
    GPIO.output(in2,GPIO.LOW)
    GPIO.output(in3,GPIO.LOW)
    GPIO.output(in4,GPIO.LOW)
    GPIO.output(in5,GPIO.LOW)
    GPIO.output(in6,GPIO.LOW)


def goFront(t):
    GPIO.output(in2,GPIO.HIGH)
    GPIO.output(in5,GPIO.HIGH)
    if(t>0):
        sleep(t)
        stopAll()
    
def goBack(t):
    GPIO.output(in1,GPIO.HIGH)
    GPIO.output(in6,GPIO.HIGH)
    sleep(t)
    stopAll()
    
def turnAClk(t):
    GPIO.output(in1,GPIO.HIGH)
    GPIO.output(in3,GPIO.HIGH)
    GPIO.output(in5,GPIO.HIGH)
    sleep(t)
    stopAll()

def turnClk(t):
    GPIO.output(in2,GPIO.HIGH)
    GPIO.output(in4,GPIO.HIGH)
    GPIO.output(in6,GPIO.HIGH)
    sleep(t)
    stopAll()

# -------------------------------------------------
# Helpers
def parse_program_arguments(description, def_nframes, def_frate):
    # Parse all program attributes
    # description:   describes program
    # def_nframes:   default number of frames
    # def_frate:     default frame rate in Hz

    parser = argparse.ArgumentParser(description=description)
    parser.add_argument('-n', '--nframes', type=int,
                        default=def_nframes, help="number of frames, default " + str(def_nframes))
    parser.add_argument('-f', '--frate', type=int, default=def_frate,
                        help="frame rate in Hz, default " + str(def_frate))
    return parser.parse_args()

# Process radar frame and return peak distance    
def getRadarDistancePeak( frame ):
    frame_data = frame[0]
    antenna_samples = frame_data[i_ant, :, :]
    distance_peak, _ = algo.compute_distance(antenna_samples)
    return distance_peak

# Stop, reverse and turn the robot.
def reverseAndTurn():
    stopAll()
    goBack(0.5)
    turnClk(0.15)

# -------------------------------------------------
# Main logic
# -------------------------------------------------
if __name__ == '__main__':

    args = parse_program_arguments(
        '''Displays distance plot from Radar Data''',
        def_nframes=0,
        def_frate=75)
    
    device1 = DeviceFmcw(uuid="00313853-314c-4c35-3135-303039303137")
    device2 = DeviceFmcw(uuid="00313853-3959-3434-3034-303331303335")
    
    #device1= DeviceFmcw(port="COM6")
    #device2= DeviceFmcw(port="COM19")
    
    i_ant = 0  # use only 1st RX antenna
    num_rx_antennas = 1

    metrics = FmcwMetrics(
        range_resolution_m=0.05,
        max_range_m=2,
        max_speed_m_s=3,
        speed_resolution_m_s=0.2,
        center_frequency_Hz=60_750_000_000,
    )

    # create acquisition sequence based on metrics parameters
    sequence = device1.create_simple_sequence(FmcwSimpleSequenceConfig())
    sequence = device2.create_simple_sequence(FmcwSimpleSequenceConfig())
    sequence.loop.repetition_time_s = 1 / args.frate  # set frame repetition time

    # convert metrics into chirp loop parameters
    chirp_loop = sequence.loop.sub_sequence.contents
    device1.sequence_from_metrics(metrics, chirp_loop)
    device2.sequence_from_metrics(metrics, chirp_loop)
    
    # set remaining chirp parameters which are not derived from metrics
    chirp = chirp_loop.loop.sub_sequence.contents.chirp
    chirp.sample_rate_Hz = 1_000_000
    chirp.rx_mask = (1 << num_rx_antennas) - 1
    chirp.tx_mask = 1
    chirp.tx_power_level = 31
    chirp.if_gain_dB = 33
    chirp.lp_cutoff_Hz = 500000
    chirp.hp_cutoff_Hz = 80000

    device1.set_acquisition_sequence(sequence)
    device2.set_acquisition_sequence(sequence)

    algo = DistanceAlgo(chirp, chirp_loop.loop.num_repetitions)
    print("Program started")
    
    while(1):
      # Move robot forward
      goFront(0)
  
      # Get distance data from radar sensor 1
      frame_contents = device1.get_next_frame()
      distance_peak = getRadarDistancePeak( frame_contents )
      print("Distance 1:" + format(distance_peak, "^05.3f") + "m")
      
      # Reverse and turn the robot if table corner is detected
      if(distance_peak > 0.25):
          reverseAndTurn()
          device1.stop_acquisition()
          device2.stop_acquisition()
  
      # Get distance data from radar sensor 2
      frame_contents = device2.get_next_frame()
      distance_peak = getRadarDistancePeak( frame_contents )
      print("Distance 2:" + format(distance_peak, "^05.3f") + "m")
      
      # Reverse and turn the robot if table corner is detected
      if(distance_peak > 0.25):
          reverseAndTurn()
          device1.stop_acquisition()
          device2.stop_acquisition()
  
      print("Still Running")

Credits

Infineon Team

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Edge Detecting Bot using XENSIV™ BGT60UTR11AIP radar

Edge Detecting Bot using XENSIV™ BGT60UTR11AIP radar

Things used in this project

Hardware components

Software apps and online services

Story

Project Block Diagram

Project Setup

Working principle

Python script overview

Working Demo

Code

Application Code

Credits

Infineon Team

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