Empirical Data Based Predictive Warning System on an Automated Guided Vehicle

Sammanfattning: An Automated Guided Vehicle (AGV) must follow protective regulations to avoidcrashing into people when autonomously driving in industries. These safety norms require AGVs to enable protective fields, which perform hard braking when objects enter aspecific area in front of the vehicle. Warning fields, or warning systems, are similar fieldsthat decrease the speed of the AGV before objects enter the protective fields to enable asteadier driving. Today at Toyota Material Handling Manufacturing Sweden (TMHMS),warning systems have been implemented but the systems are too sensitive to objects outside of the AGVs path.The purpose of this thesis is to develop a predictive warning system based on empiricaldata from previous driving scenarios. By storing previous positions, the warning systemcould estimate a trajectory based on simple statistics and deploy speed limiting decisionsif objects appear in the upcoming predicted path.The predictive warning system was compared to the current warning system and adeactivated warning system setup in driving performance and driving dynamics. Performance was measured by measuring time to finish an industry-like test track and dynamicswas subjectively rated from a group of experienced AGV developers from TMHMS. Results showed that a predictive warning system drove the test track faster and with betterdynamics than the current warning system and the no warning system setup.Key findings are that a predictive warning system based on empirical data performedbetter in most cases but has some extra requirements to function. Firstly, the method require the AGV to mostly drive on previously driven paths to produce good results. Secondly the warning system requires a somewhat powerful on board computer to handlethe computations. Finally, the warning system requires spatial awareness of pose for thevehicle, as well as structure and shape for deployed protective fields.