Autonomous and assistive algorithms for log-loading operations on Crane test-bed

Timber-harvesting is an important industry to Canada. At this time, the industry is predicting serious labour shortages over the coming decade. Because of this, the industry is looking to robotics and AI technologies to increase the level of autonomy of timber-harvesting machines, as well as, to make the operations more safe and environment friendly. In this project, we will carry out research and development on increasing the autonomy and intelligence of log-loading operations in the timber supply chain from the forest to the mill yard. We will specifically focus on the log grasping problem which is the key element of both the log-loading and unloading operations. In this context, we will develop methods to enable autonomous grasping of multiple logs in a wide variety of conditions and use-cases, thereby advancing the state-of-the-art of research on this problem. We will also explore opportunities, develop concepts and user interfaces to provide assistance to operators of log-loading machines, as an intermediate step to autonomy. The algorithms and methodologies will be evaluated by using the Crane test-bed available at our partner institution FPInnovations.

Faculty Supervisor:

Inna Sharf

Student:

Partner:

FPInnovations (Pointe-Claire, QC)

Discipline:

Engineering

Sector:

Agriculture; Manufacturing; Professional, scientific and technical services

University:

McGill University

Program: