Altium on Antoine Martin's Blog

DuploBuster

Wed, 14 Jun 2023 00:00:00 +0000

Competition Specifications

The goal of this competition is to create an autonomous robot able to gather Duplo toys and drop them in the collection zone of the 8x8 meters arena that features obstacles and different terrains. The room have four different regions with different color LEDs in every corner. The robot have 10 minutes inside the room to collect as many Duplo as it can.

How the robot collects Duplo

A claw grips the toy and elevates it into it’s compartment
Once the Duplo has picked up enough toys it releases them at the dropping zone by opening it’s door
The front camera uses machine learning to recognize Duplos while a LIDAR scans the environment for obstacles
A 360° top camera gives the position and angle while an A* algorithm deduces it’s trajectory

Object Detection

Problem Introduction

The duplo are localized using deep learning. In fact the Duplo comes in big variety of colors and shapes, making feature detection algorithm impractical in this situation.

Dataset

Pictures of Duplo in the arena were captured using the same camera (Raspberry Pi Camera V2.1) that would then eventually be used for the inference.

A total of 600 pictures were capture, after deleting the non usable one, 523 remained.

The dataset was labelled on the free to use online platform Roboflow.

Policy Training

The framework used for target identification is YoloV6 (YOLOv6-N to be exact), it is the lightest and fastest to run model among all others (works with \textbf{640x640} pixels pictures). The policy was trained on Google Colab and exported to the ONNX format for inference.

Results

The raw inference performance are the following:

True positive	False positive
83%	0%

Electronics

Sensor list

Ultrasonic Distance Sensor HCS04 x 2
Microswitch Sensor x 4
MPU-9150
RPLIDAR A1M8
Raspberry Pi Camera V2.1
Webcam Logitech C720

Actuator list

Maxon EC 32 flat (15 W), 1:60 gearbox
DC motor x 2
Stepper motor
Buzzer

Most of those sensors and actuators are interfaced through I2C, SPI, PWM and Serial.

A STM32F4 is used for interfacing with the sensors and actuators in realtime.

A Raspberry Pi 4 Model B is used for more resource intensive task (e.g. computer vision).

Connections diagram

Electronic assembly

Maxon 24/2 controller
DC Driver 15x2 Lite
Raspberry Pi Camera V2.1
Raspberry Pi 4 Model B
Nucleo F446
Breadboard + IMU + AMIS-30543
Power Board

Custom PCB

connects sensors and actuators with the MCU and Raspberry Pi.

Some hardware details

LIDAR

The claw (grasps the Duplo)

Driving wheels

The front wheels are 3D printed using TPU

Sensor Acquisition System

Sat, 11 Sep 2021 00:00:00 +0000

Introduction

Requirements:

Support up to 16 sensors simultaneous
Support different kind of sensors, must be easily upgradable to support new ones
Independent conditioning of the signal for each sensor (1 board per sensor)
Transmit the data to the computer which will log it
Settings must be set from the computer
Sampling rate of 1kHz
Resolution of 16bits
Required supported sensors:
- force sensor using Wheastone bridge
- Thermocouple
- Pressure sensor

Project Architecture

Components

The list of components used has changed quite a lot thorough the design, I will for the sake of clarity only talk about the final design choice.

Analog to Digital Converter

Accordingly to the requirements, the ADC must have 16 physical pins to which the sensors would be always connected. Moreover it has to sample each 16 sensors at a resolution of 16 bits at 1kHz.

MCU

The MCU must validates the following requirements:

2 SPI interface of at least 256 kbits/s (one for the ADC, the other for the USB port)
be of the STM32 family (since it’s the one mostly used at the ERT)
handle a SPI data traffic of 256 kbits/s from the ADC

In order to reduce the workload on the PCB design, the MCU must be already assembled on a board featuring :

USB interface for transmitting data/debugging/programming of the MCU
Header pins such that it can plugged as a shield on the main PCB board

The chosen MCU is the Nucleo STM32 F446RE.

Power Supply

Many voltages are needed : 5V, 3.3V, 9V, 12V.

Hostboard A

PCBs assembly

Assembled PCBs with test pcb

Daugtherboard

PCBs assembly

Rocket Avionics - EPFL Rocket Team

Sat, 22 Feb 2020 00:00:00 +0000

Project Description

The IO Rocket was a ~ 1.8 meters rocket made by members of the [https://epflrocketteam.ch/our-projects/io/](EPFL Rocket Team - ERT).

It wasn’t designed for competition purposed but rather to let us learn new skills as many of us building the rocket were in their first or second year of bachelor.

I’ve personnaly worked under the avionic sub-system, more precisely I was tasked to build to PCBs responsible for telemetry and triggering the second explosive charge (to deploy the second chute).

It was for me the opportunity to learn the software Altium Designer, a schematic and PCB design software

Due to the COVID-19 pandemic, the IO project was ended sooner than expected and we never had the opportunity to rebuild a second enhanced version.

In fact, the only IO rocket we’ve launched was really rough, see below ;)

Some insights of the avionic

Components:

IMU : attitude
BMP180 : pressure, altitude, temperature
Arduino nano : MCU
Lora : Radio

Altium on Antoine Martin's Blog

DuploBuster

Competition Specifications

How the robot collects Duplo

Object Detection

Problem Introduction

Dataset

Policy Training

Results

Electronics

Sensor list

Actuator list

Connections diagram

Electronic assembly

Custom PCB

Some hardware details

LIDAR

The claw (grasps the Duplo)

Driving wheels

Sensor Acquisition System

Introduction

Requirements:

Project Architecture

Components

Analog to Digital Converter

MCU

Power Supply

Hostboard A

Daugtherboard

PCBs assembly

Rocket Avionics - EPFL Rocket Team

Project Description

Some insights of the avionic

Memories..