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SLO - Self-learning drone

Published: 26 February 2020

In this project, Machine Design carries out a doctoral project funded by the SLO-fund (The Royal Swedish Academy of Agriculture and Forestry), where a self-learning system for automated route planning and navigation of agricultural machines is developed.

Robotization and automation have, in the agricultural industry and other industries shown great potential in terms of resource efficiency, quality, work environment, accident risk, etc. In recent years, research and development have led to many new methods, tools and technologies in automation, but most Swedish agricultural machines are still controlled by drivers on board. Elimination of this staffing is central to the future sustainable agriculture and in this project a self-learning system for automated route planning and navigation of agricultural machines under Swedish conditions will therefore be developed in a doctoral project over four years.

Project: Autonomous Navigation for articulated vehicles in agriculture

Agriculture is vital part of our food chain. Mechanization and automation has been continuously improving agricultural productivity, profitability and working conditions over the years. Automation of agricultural vehicles/machines is seen as to have potential to solve today’s several challenges of agricultural sustainability. Full autonomy in agricultural vehicle is yet to achieve goal which means no human intervention or local supervision needed for machines to carry out agricultural field operations.

One crucial requirement for full autonomy is to relieve the human operator from agricultural vehicles. Instead, the vehicle could plan and navigate autonomously in agricultural environment without human intervention.Therefor, this research  focuses on development of autonomous navigation system for off-road articulated research platform developed at LTU. A part of it being tested in October 2020. https://www.youtube.com/watch?v=ynbQyQcfSM0

PhD Student: Saira Latif https://www.ltu.se/staff/s/sailat-1.184847

Supervisor: Magnus Karlberg https://www.ltu.se/staff/m/magkar-1.10404