Tendon-driven Grasper Design for Aerial Robot Perching on Tree Branches

TL;DR

The paper tackles energy-efficient data collection by aerial robots operating in dense forests, where continuous flight is costly. It combines a tendon-driven, bat-inspired gripping mechanism with a raptor-inspired vision system to locate suitable branches and a minimum-snap trajectory planner for controlled contact. Key contributions include a modular, passive-compliant perching claw design, a static contact model for payload prediction with $M=k\Delta\theta$ and $F_{max}=\frac{\sigma_{UTS} A r(\bar{r}-R)}{(R-r)L}$, a DeepLabV3+-based trunk/branch segmentation pipeline, and a 7th-order-Polynomial trajectory planner validated in real-world tests. The results show successful perching on branches with diameters from $30$ to $80\,\text{mm}$, with 3D-position tracking errors around $0.12$ m, demonstrating a practical method to extend forest monitoring endurance.

Abstract

Protecting and restoring forest ecosystems has become an important conservation issue. Although various robots have been used for field data collection to protect forest ecosystems, the complex terrain and dense canopy make the data collection less efficient. To address this challenge, an aerial platform with bio-inspired behaviour facilitated by a bio-inspired mechanism is proposed. The platform spends minimum energy during data collection by perching on tree branches. A raptor inspired vision algorithm is used to locate a tree trunk, and then a horizontal branch on which the platform can perch is identified. A tendon-driven mechanism inspired by bat claws which requires energy only for actuation, secures the platform onto the branch using the mechanism's passive compliance. Experimental results show that the mechanism can perform perching on branches ranging from 30 mm to 80 mm in diameter. The real-world tests validated the system's ability to select and adapt to target points, and it is expected to be useful in complex forest ecosystems.

Figures (9)

• Figure 1: An example of aerial robotic platforms inhabiting real natural environments.
• Figure 2: Design and operation of the perching mechanism. (a) The close and open state is achieved through tendon actuation and a servo motor. (b) Claw design. (c) Claw pad for additional friction. (d) Friction pad with fingerprint pattern. (e) Elastic joint.
• Figure 3: Interaction profile between the branch and the perching mechanism depends on the branch diameter. The green dots show the contact points from an axial view. (a), (d) Small branches. Contact forces are shown in black arrows and the corresponding friction forces are shown in red. (b), (e) Medium branches. Internal joint forces are also shown. (c), (f) Large branches.
• Figure 4: Static analysis of the perching mechanism in contact with a target. (a) Predicted payload capacity, the maximum take-off weight is determined by the motor's capacity, while the current platform weighs about 1500 g (b) Simplified static interaction model used for static model.
• Figure 5: Flow chart of visual segmentation algorithms. Using the visual information from the depth camera as input, the spatial information of the perching point is computed iteratively, and the coordinates of the centre point of the target to be housed are output sequentially.