Podcast: Teaching Robots To Find Buried Objects

19 Jul, 2022

Peter Mitrano demonstrates a rope manipulation experiment. Software that he and Berenson developed can expand training data sets for challenges like rope manipulation, doubling the success rate of the robot. Credit: Daryl Marshke, Michigan Creative, University of Michigan.

In this episode, we talk about how engineers are tackling the problem of sorting through piles using robotics and a platform that leverages fake data to train robots more efficiently.

big data

- computer vision

- machine learning

- robot operating system (ROS)

- robotic arms

In this episode, we talk about how engineers are tackling the problem of sorting through piles using robotics and a platform that leverages fake data to train robots more efficiently.

This podcast is sponsored by Mouser Electronics.

EPISODE NOTES

(0:57) - Robots Finding Buried Objects🤖🔑

(16:50) - Fake Data To Train Robots🤖🧠

About the podcast:

Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell & Farbod Moghaddam who discuss the most interesting content in this podcast series.

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A Better Solution For GPS Waypoint Navigation

If you’re unfamiliar with the term, GPS Waypoint Navigation is the ability to provide a robot with a set of GPS waypoints (i.e., a set of latitude / longitude pairs), and have the robot autonomously navigate from its current location to each of the defined waypoints.

Tim Peckover

fromClearpath Robotics

05 Mar, 2020

machine learning

- robot operating system (ROS)

For outdoor robotics, one of the key capabilities for autonomous use-cases is GPS Waypoint Navigation.

If you’re unfamiliar with the term, GPS Waypoint Navigation is the ability to provide a robot with a set of GPS waypoints (i.e., a set of latitude / longitude pairs), and have the robot autonomously navigate from its current location to each of the defined waypoints. An added bonus would be not bumping into anything along the way.

This has been done in research projects and papers a number of times, but scalable hardware and software packages to enable this on production ready field robots are few and far between.

That’s why Clearpath and Veerum, with the help of a Mitacs grant, partnered with Steve Phillips, a civil engineering graduate student from the University of Waterloo. Steve’s research is a cross between mechanical and structural engineering, focusing on using mobile robots to perform infrastructure assessment and inspection. For this project with Clearpath, he’s developed a robust, repeatable GPS Waypoint Navigation system for Clearpath’s family of outdoor robots.

Over the past semester, Steve has been working away at selecting and integrating the right payload suite, perfecting the fusion of sensor data, and creating a usable interface to easily select a waypoint and send the robot on its way.

THE SETUP

For development and testing of the GPS navigation software, Steve’s primary setup consisted of a Jackal UGV, paired with the rugged Duro GPS from Swift Navigation and the UM7 inertial measurement unit (IMU) from Redshift Labs. In addition to supporting high accuracy RTK, the Duro includes a built in Bosch BMI160 IMU and BMM150 Magnetometer. To verify that the software functions hardware agnostically, Steve and the Clearpath software team also deployed it on the much larger Warthog UGV (outfitted with a Novatel GPS and a Lord GX5-25 IMU). Running autonomy s

The Bin-Picking Handbook For Engineers

This handbook introduces the concept of bin-picking, and explores its complexity through real-case studies.

Morgan Talatizi

fromVisio Nerf

14 Jan, 2022

automation

- computer vision

- grippers

- robot operating system (ROS)

- robotics

The automation of a production line has become a major issue for many industries, and the need to increase productivity while having a good return on investment has caused many concerns. The main area in which these challenges are tackled is bin-picking.

Moving a part from a container to place it to a specific position, ready for the next step, sounds very simple, but it becomes complex when you need to abide to cycle-time requirements.

As demand for accurate, autonomous and flexible bin-picking systems increase, Visio Nerf has produced a handbook to the technology designed introduce key concepts and offer concrete examples of cutting-edge technology. Below are some of thehighlights from the handbook. The book guide can be downloaded here.

Introduction to Bin-Picking

The automation of production cells is at the heart of current concerns for many industries, the main objective being to find the right balance between investment and improved productivity. One of the operations for which the evolution towards a high-performance automated production tool has become necessary is Bin-Picking.

Bin-picking involves moving a part from its original position to a specific delivery zone to make it available for the next production step. The workpiece is initially placed in a container or on a conveyor in a random, semi or fully-ordered manner depending on the application. The main challenge of Bin-Picking is to offer an efficient automated solution to meet cycle time optimization requirements, to protect operators from difficult and repetitive tasks particularly in harsh environments while increasing the flexibility and productivity of the work cell. Technological advances in the field of machine vision and calculation software can now provide solutions adapted to most industrial applications.

Essential criteria for assessing bin-picking

In order to assess the efficiency of a Bin-Picking cell and determine its dimensionin

Podcast: Teaching Robots To Find Buried Objects

In this episode, we talk about how engineers are tackling the problem of sorting through piles using robotics and a platform that leverages fake data to train robots more efficiently.

EPISODE NOTES

THE SETUP

Introduction to Bin-Picking

Essential criteria for assessing bin-picking

A robot that finds lost items

What Does Robotic No-Code Programming Look Like

Removing Personally Identifiable Information From Video Streams

In this episode, we talk about how engineers are tackling the problem of sorting through piles using robotics and a platform that leverages fake data to train robots more efficiently.

EPISODE NOTES

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