Reliable object localization is a critical challenge in drone-based aerial manipulation, particularly when objects are outside the camera's field of view. This paper presents a new approach to enhance drone reliability in aerial grasping tasks by integrating a 1D time-of-flight range sensor with a vision-based localization system. The range sensor, positioned beneath the drone, generates a detailed point cloud of the ground beneath the drone, allowing for precise object localization even when the drone hovers directly above the target. By combining visual tracking with realtime distance measurements, our system achieves a 96 % grasp success rate across 128 trials with diverse objects, representing a significant improvement over previous approaches. This method enables zero-shot grasping without prior knowledge of the objects, increasing versatility and robustness in complex, unstructured environments. The open-source software and hardware design of the platform provide a foundation for further research and development in the field of autonomous aerial manipulation.

Rapid aerial grasping through robots can lead to many applications that utilize fast and dynamic picking and placing of objects. Rigid grippers traditionally used in aerial manipulators require high precision and specific object geometries for successful grasping. We propose RAPTOR, a quadcopter platform combined with a custom Fin Ray®gripper to enable more flexible grasping of objects with different geometries, leveraging the properties of soft materials to increase the contact surface between the gripper and the objects. To reduce the communication latency, we present a new lightweight middleware solution based on Fast DDS (Data Distribution Service) as an alternative to ROS (Robot Operating System). We show that RAPTOR achieves an average of 83% grasping efficacy in a real-world setting for four different object geometries while moving at an average velocity of 1 m/s during grasping. In a high-velocity setting, RAPTOR supports up to four times the payload compared to previous works. Our results highlight the potential of aerial drones in automated warehouses and other manipulation applications where speed, swiftness, and robustness are essential while operating in hard-to-reach places.11Code: https://github.com/raptor-ethz/raptor_setup