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

5 DOFs Robotic Arm

Fri, 01 Jun 2018 00:00:00 +0000

Description

⚠️ Warning

WARNING: This post describes a project known to be of very low quality. You might experience nausea while reading it.

.. This is an old project from high-school. Back then I had no idea about how to properly engineer anything, more specifically:

how to properly design a robot (e.g: torque balancing)

concurrent motor control (everything runs in a single thread..)

inverse kinematic, it is totally missing here -_-

This project is about designing and manufacturing a simple 5 DOFs robotic arm.

It uses one servomotor for each degree of freedom, all controlled and powered by the Arduino Mega 2560.

There are 4 push buttons that can be used to manually control each degree of freedom separately.

C on Antoine Martin's Blog