Rapid close surrounding evaluation for autonomous commercial vehicles

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/238916
Download file(s):
File Description SizeFormat 
238916.pdfFulltext1.58 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Rapid close surrounding evaluation for autonomous commercial vehicles
Authors: Bagchi, Shamit
Soultani, Evangelia
Abstract: A framework has been developed for local, relative navigation of an autonomous commercial vehicle in confined areas. This enables a vehicle to navigate based only on its immediate surroundings, with the vehicle as the reference. The framework uses a simulator, to simulate the environment of a mine, and a vehicle model. The latter is equipped with 2D LIDAR sensors which allow it to interpret its close surroundings. Based on this information, a navigation algorithm is selected which performs the path planning and moves the vehicle. Four such algorithms have been developed in order to handle two distinct scenarios: navigation in a corridor and navigation at an intersection. The algorithms have been evaluated for safety and accuracy and perform consistently well. The overall root mean square deviation from a reference path is less than 25% of a chosen collision threshold. Throughout the evaluation, a safe distance from all the walls was maintained. In addition, the path generation algorithms were highly efficient with an average execution time of under 2 milliseconds. This work opens up the possibilities for algorithms which can handle additional navigation scenarios and can learn and adapt to its environment.
Keywords: Farkostteknik;Transport;Vehicle Engineering;Transport
Issue Date: 2016
Publisher: Chalmers tekniska högskola / Institutionen för tillämpad mekanik
Chalmers University of Technology / Department of Applied Mechanics
Series/Report no.: Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2016:40
URI: https://hdl.handle.net/20.500.12380/238916
Collection:Examensarbeten för masterexamen // Master Theses



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.