Grant: Alliance Grants
University of Windsor
$50,000.00
Learning-based autonomous robotic system for package sorting application
FY 2022-23
Summary
With the advent of e-commerce and logistics industry, numerous goods are delivered every day. The COVID-19 pandemic-driven lockdown has forced businesses and customers to use e-commerce systems in order to sell or purchase products and services online. Sorting systems play a pivotal role in orders delivery practices. Typically, sorting systems consist of a mix of conveyors tied together which are controlled by the Warehouse Management System (WMS). Traditional objects sorting lines rely on human operators to locate and pick each product by hand. Such systems are labour-intensive, time-consuming, and error-prone, resulting in multiple counts to be done on a regular basis. To remedy these problems, automated sorting systems have been adopted. Despite the previous efforts, most robotic sorting systems fail to work in a complex environment for real-life applications. In these situations, robots should be able to recognize objects with various physical characteristics accurately and sort them quickly. To this end, an autonomous sorting robot should have the following capabilities: (i) detection and classification of objects with different shapes, sizes and physical properties, (ii) optimal object grasping, and (iii) trajectory generation and motion planning within the 3D environment. To fulfill these requirements, we will combine state-of-the-art computer vision algorithms with artificial intelligence methods to develop an autonomous robotic system for sorting packages in a real-world environment. This project will considerably benefit Canada in the following ways. (i) A wide range of Canada's industries will benefit from the proposed automated sorting system. The outcomes of this project have positive impacts on production, warehousing, inventory planning, and transportation sectors. (ii) The need for state-of-the-art technologies in this project will increase job opportunities for High Quality Personnel (HQP) in Canada and train them for real-world engineering applications. (iii) This project will precipitate the growth of Canada's e-commerce and logistic industry to increase its market share in the global market.
Program
Alliance Grants
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
575195-2022
Name nom
Alirezaee, Shahpour SDR
Department département
Mechanical, Automotive and Materials Engineering
Organization id
37
Institution établissement
University of Windsor
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2022
Award amount
50000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1206
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Engineering
Area of application fr
Génie
Research subject code
2601
Research subject group en
Robotics
Research subject group fr
Robotique
Research subject en
Robotic control
Research subject fr
Systèmes de commande
Installment
inst:1
Partie
1
Nb partie
1
Application title
Learning-based autonomous robotic system for package sorting application
Application summary
With the advent of e-commerce and logistics industry, numerous goods are delivered every day. The COVID-19 pandemic-driven lockdown has forced businesses and customers to use e-commerce systems in order to sell or purchase products and services online. Sorting systems play a pivotal role in orders delivery practices. Typically, sorting systems consist of a mix of conveyors tied together which are controlled by the Warehouse Management System (WMS). Traditional objects sorting lines rely on human operators to locate and pick each product by hand. Such systems are labour-intensive, time-consuming, and error-prone, resulting in multiple counts to be done on a regular basis. To remedy these problems, automated sorting systems have been adopted. Despite the previous efforts, most robotic sorting systems fail to work in a complex environment for real-life applications. In these situations, robots should be able to recognize objects with various physical characteristics accurately and sort them quickly. To this end, an autonomous sorting robot should have the following capabilities: (i) detection and classification of objects with different shapes, sizes and physical properties, (ii) optimal object grasping, and (iii) trajectory generation and motion planning within the 3D environment. To fulfill these requirements, we will combine state-of-the-art computer vision algorithms with artificial intelligence methods to develop an autonomous robotic system for sorting packages in a real-world environment. This project will considerably benefit Canada in the following ways. (i) A wide range of Canada's industries will benefit from the proposed automated sorting system. The outcomes of this project have positive impacts on production, warehousing, inventory planning, and transportation sectors. (ii) The need for state-of-the-art technologies in this project will increase job opportunities for High Quality Personnel (HQP) in Canada and train them for real-world engineering applications. (iii) This project will precipitate the growth of Canada's e-commerce and logistic industry to increase its market share in the global market.
