Public defence in Automation, Systems and Control Engineering, M.Sc. Tabish Badar

The title of the thesis: Enabling sustainable and cost-efficient semi-autonomous forest machine chain - Modeling, estimation and control for autonomous driving in terrain

Thesis defender: Tabish Badar
Opponents: Prof. Jouni Mattila, Tampere University and Prof. Kalle Kärhä, University of Eastern Finland
Custos: Prof. Arto Visala, Aalto University School of Electrical Engineering, Department of Electrical Engineering and Automation

Due to climate change, the forest ground will be frozen shorter time or not at all, which creates challenges for forest harvesting, particularly for the forest forwarders, which transport logs from the forest to the forest road. Lighter forwarders are needed. In the Autologger -project a novel forest machine chain was introduced, which consists of a human-driven harvester and two lighter autonomous forwarders. In this Dr dissertation, required measurements and algorithms needed in the smart harvester and algorithms for forwarders for autonomous driving in terrain were developed. The harvester shows the spatial driving paths for forwarders and simultaneously measures a 3D model of the solid ground of the paths needed in rollover avoidance of autonomous forwarders. For autonomous driving in terrain, generic dynamic models and state estimation of the vehicle and nonlinear model predictive control for steering in path tracking and velocity in rollover avoidance were developed and tested in different test tracks with Polaris e-ATV, developed for research of automatic driving in terrain.

Keywords: Autonomous ground vehicles, 3D path estimation, vehicle modeling and simulation, model validation, nonlinear Kalman filtering, nonlinear model predictive control

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

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Doctoral theses in the School of Electrical Engineering: https://aaltodoc.aalto.fi/handle/123456789/53