Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators

Featured Application The main idea of this paper was to deploy a team of unmanned aerial vehicles (UAVs) to attach a sensor to a bridge using a two-component adhesive in order to perform an inspection. Constant pressure must be applied for several minutes to form a bond between two adhesives. Therefore, one UAV sprays the colored component of an adhesive while the aerial manipulator transports the sensor, detects the contact point and attaches the sensor to it. A trajectory planning algorithm was developed around the dynamic model of the UAV and the manipulator attached to it, ensuring that the end-effector is parallel to the wall normal. Finally, the aerial manipulator achieves and maintains contact with a predefined force through an adaptive impedance control approach. Periodic bridge inspections are required every several years to determine the state of a bridge. Most commonly, the inspection is performed using specialized trucks allowing human inspectors to review the conditions underneath the bridge, which requires a road closure. The aim of this paper was to use aerial manipulators to mount sensors on the bridge to collect the necessary data, thus eliminating the need for the road closure. To do so, a two-step approach is proposed: an unmanned aerial vehicle (UAV) equipped with a pressurized canister sprays the first glue component onto the target area; afterward, the aerial manipulator detects the precise location of the sprayed area, and mounts the required sensor coated with the second glue component. The visual detection is based on an Red Green Blue - Depth (RGB-D) sensor and provides the target position and orientation. A trajectory is then planned based on the detected contact point, and it is executed through the adaptive impedance control capable of achieving and maintaining a desired force reference. Such an approach allows for the two glue components to form a solid bond. The described pipeline is validated in a simulation environment while the visual detection is tested in an experimental environment.