Development and evaluation of AI-enabled planning algorithms for timber-harvesting machines using Vortex

Timber-harvesting is an important industry for Canada. For tree-cutting specifically, the industry employs large heavy-duty machines with crane-like manipulator arms which are driven by trained operators. The work involved in operating these machines is tiring, involves long hours in harsh conditions and as a result, there is presently a labour shortage for operators. It is therefore imperative to introduce AI and robotics into the operation of these machines in order to reduce the workload on the operators. In this project, we will be developing such algorithms for feller-buncher, harvester and log-loading machines. Our goal is to provide assistive technologies as an aid to the operator for planning the motion of the machine during the tree-cutting and log loading tasks. We will be implementing our algorithms on a training simulator at our industrial partner’s (CM Labs) premises and will use the simulator to conduct studies with operators. These studies will allow us to evaluate the benefits and deficiencies of our algorithms and identify further directions for this research. Through this project, we will also expand the c

Faculty Supervisor:

Inna Sharf

Student:

Partner:

CMLabs

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

McGill University

Program: