Distributed tactile sensing for multiforce detection is crucial for various aerial robot interaction tasks. However, current contact sensing solutions on drones only exploit single end-effector sensors and cannot provide distributed multicontact sensing. Designed to be easily mounted at the bottom of a drone, we propose an optical tactile sensor that features a large and curved soft-sensing surface, a hollow structure and a new illumination system. Even when spaced only 2 cm apart, multiple contacts can be detected simultaneously using our software pipeline, which provides real-world quantities of 3-D contact locations (mm) and 3-D force vectors (N), with an accuracy of 1.5 mm and 0.17 N, respectively. We demonstrate the sensor's applicability and reliability onboard and in real time with two demos related to, first, the estimation of the compliance of different perches and subsequent realignment and landing on the stiffer one, and second, the mapping of sparse obstacles. The implementation of our distributed tactile sensor represents a significant step toward attaining the full potential of drones as versatile robots capable of interacting with and navigating within complex environments.

Forests provide vital resources and services for humanity, but preserving and restoring them is challenging due to the difficulty of obtaining actionable data, especially in inaccessible areas, such as forest canopies. To address this, we follow the lead of arboreal animals that exploit multiple modes of locomotion. We combine aerial and tethered movements to enable AVOCADO to navigate within a tree canopy. Starting from the top of a tree, it can descend with the tether and maneuver around obstacles with thrusters. We extend our previous work with a new mechanical design with a protective shell, increased computational power and cameras for state estimation. We introduce a dynamic model and simulation, and perform a quasistatic and dynamic validation. For autonomy, we derive a control framework in simulation to regulate tether length, tilt, and heading, before transfer to the robot. We evaluate the controllers for trajectory tracking through experiments. AVOCADO can follow trajectories around obstacles and reject disturbances on the tether. Exploiting multimodal mobility will advance the exploration of tree canopies to actively monitor the true value of our forests.