A CNN-based Approach for Cable-Suspended Load Lifting with an Autonomous MAV

• Published in: Journal of intelligent & robotic systems 2022-06, Vol.105 (2), Article 32
• Main Authors: Lopez, Manuel, Martinez-Carranza, Jose
• Format: Article
• Language: English
• Subjects: Air currents; Analysis; Artificial Intelligence; Artificial neural networks; Control; Controllers; Cost control; Delivery services; Detectors; Electrical Engineering; Energy consumption; Engineering; Hoisting; Image acquisition; Mechanical Engineering; Mechatronics; Micro air vehicles (MAV); Neural networks; Object recognition; Packages; Robotics; Robots; Sediment transport; Sensors; Short Paper; State machines; Success; Suspended load; Topical collection on Unmanned Systems; Vehicles; Vision systems
• ISSN: 0921-0296; 1573-0409
• DOI: 10.1007/s10846-022-01637-w

The popularity of Micro Aerial Vehicles (MAV) to be used in civilian applications has increased in the last years. However, in most of these applications, a MAV is used to acquire aerial images and video of areas and structures of interest. However, MAVs could become more useful if they can interact with the environment. For instance, in a parcel delivery task, the goal is for the MAV to deliver a package somewhere, but what about having to pick up a package autonomously? This task raises some challenges: i) the MAV has to recognize where the package or object of interest is; ii) the MAV has to plan its maneuver to achieve the picking. In this paper, we address both challenges, considering the scenario where the MAV has a suspended cable that moves freely with a hook attached at the end of the cable. A suspended cable saves weight, although it has to be indirectly controllable with the MAV’s flight. Thus, we present a solution based on a Convolutional Neural Network that is trained to recognize the object of interest, in this case, a bucket; and that simultaneously recognizes the hook. Both objects are expected to be observed with a camera on board the MAV. Our method uses the distance between these two objects in a state machine controller to position the MAV and trigger the lifting maneuver in a single upward motion action that reduces the effects of air current on the hook. We use synthetic datasets to train the bucket and hook detector, but the model is capable of performing the detection in real environments. We achieved an average lifting success rate of 70 % for indoor and 60 % for outdoor scenarios.