Lifelong mapping for dynamic factory floor environments
Mapping the factory floor environments
The HR-Recycler project aims to automate the transportation of WEEE devices and disassembled components within a collaborative factory floor environment. To achieve this, different type of AGVs dedicated to serve specific roles during the disassembly process will be developed. To enable AGVs navigation in such environments, simultaneously localization and mapping (SLAM) methods will be utilized. However, when it comes to highly dynamic environments such as factory floors with moving objects the built maps with classic SLAM are soon get obsolete and the robot navigation performance degrades.
Despite the leaps of progress that have been made in the field of mobile robotics in recent years, one major challenge that AGVs still face is that of long-term autonomous operation in dynamic environments. In the HR-Recycler scenario where the AGV operates in a factory floor (see Figure 1), it has to deal with changing conditions where other robots, workers, moving objects such as pallets and even commodities move around the factory environment. In this scenario with the typical SLAM mapping the static objects (such as walls) will constitute only a fraction of the existing information in the map during robot navigation. If we consider this map as the ground truth, and use it disregarding ongoing changes, it is very challenging to maintain stable robot localization even if a robust localization filter will be employed.
Figure 1 Gazebo simulation of the BIANATT S.A. (End-User) factory floor environment. Note that the majority of the existing information in the illustrated infrastructure corresponds to dynamic objects.
Robotic Solution: Life-long mapping
CERTH-ITI developed an essential solution on solving this issue by employing the life-long mapping approach which will has the ability to distinguish static and dynamic areas and handle this information accordingly during robot navigation. The ability to identify areas that exhibit high or low dynamics can improve the navigation of mobile robots as well as improve the process of long-term mapping of the environment. We utilized temporal persistence modeling in order to predict the state of cells in the life-long map by gathering rare observations from the on-board the robot sensors. This allows the modeling of the objects persistence in the map and provides to the system with prior knowledge regarding the occupancy of the area where robot operates. The method allows robot navigation by avoiding congested areas deteriorating the re-plans leading thus the robot to its target location without unnecessary maneuvering. Simulation results on life-long mapping with temporal persistence modelling are outlined in Figure 2, which illustrates the static metric map (left) and the probability of the dynamic areas through temporal persistence modeling (right).
The Visual Analytics Lab (VARLab) of CERTH-ITI
Figure 2 The outcome of life-long mapping . The left image illustrates the metric map produced by the classical 2D SLAM, while the right image corresponds to the dynamic obstacles persistence probability calculated from the temporal persistence modeling.