Adaptive Warning Field System
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
Student thesis
Abstract [en]
This thesis is based on the work carried out in the field of safety systems for Autonomous Guided Vehicles(AGV). With autonomous vehicles being more prominent today, safe traversing of these is a major concern. The same is true for AGVs working in industry environment like forklift trucks etc. Our work applies to industrial robots. The method described here is developed by closely following an algorithm developed for safe traversing of a robot using a warning field. The report describes the literature review with work related to the safe traversing, path planning and collision avoidance in robots. The next part is dedicated to describing the methodology of implementation of the Adaptive Warning Field Method and the Dynamic Window Approach. The evaluation of the Adaptive Warning Method with the previous developed Warning Field Methods is done and test cases are designed to test the working of the designed method. Vrep simulation environment and Industrial data is used to run a simulation of the robot using the method developed in this work. We find that the method performs better compared to the previous methods in the designed scenarios. Lastly we conclude the report with the future work that can be carried out to improve and extend the algorithm.
Place, publisher, year, edition, pages
2017. , p. 90
Keywords [en]
Automated Guided Vehicles (AGVs), Adaptive Warning Field, Collision Avoidance, Dynamic Window Approach(DWA), F45, Static Protection and Warning Fields.
National Category
Computer Systems Robotics
Identifiers
URN: urn:nbn:se:hh:diva-35312OAI: oai:DiVA.org:hh-35312DiVA, id: diva2:1153906
Subject / course
Computer science and engineering
Educational program
Master's Programme in Embedded and Intelligent Systems, 120 credits
Presentation
2017-09-26, F5, Kristian IV:s väg 3, 301 18, Halmstad, 14:00 (English)
Supervisors
Examiners
2017-11-012017-10-312017-11-01Bibliographically approved