Transporting Dispersed Cylindrical Granules: An Intelligent Strategy Inspired by an Elephant Trunk
Manipulating granular materials is a crucial function of robotic grippers. However, the existing approaches always suffer from low efficiency when dealing with large quantities of dispersed granules. To overcome this challenge, inspiration is drawn from an African elephant (Loxodonta Africana), which can employ both fingertip extensions on the trunk tip to efficiently grasp dispersed granular food all at once by mediating state transition of granules. Herein, this bio-inspired intelligent strategy is integrated into a soft pneumatic gripper for transporting dispersed granules. To evaluate the critical actuation pressures while grasping granules, a library is constructed experimentally, and the effects of the initial relative height and relative lifting speed on the grasping success rate are examined. It is indicated in the experimental results that this trunk-inspired robotic strategy leads to a success rate of over 90% and saves ≈50% duration of manipulation compared to the individual gripping fashion. Herein, new insights may be offered in this study into a novel manipulation strategy for efficiently transporting dispersed granular materials.
Manipulating granular materials is a critical capability for robotic grippers. However, existing manipulation strategies often suffer from inefficiency when handling large quantities of dispersed granules. To address this challenge, this study draws inspiration from the foraging behavior of African elephants and proposes an intelligent strategy for manipulating loose granular materials.
The difficulty of manipulating granular materials is closely tied to their intrinsic physical characteristics. Unlike individual particles, granule ensembles exhibit properties of both solids and fluids. Without external constraints, they behave like fluids, while under geometric or force confinement, they exhibit solid-like properties. Furthermore, they can transition between these two phases, which poses a significant challenge for existing robotic transport technologies.
To tackle this issue, the research team turned to the African elephant. As a voracious feeder, an elephant consumes up to 200 kg of food per day. Among this intake, granular food (such as elephant grass, peanuts, and pellets) accounts for approximately 10%, estimated to comprise around 69,000 granules.
If the elephant were to pick up each granule individually (assuming each pickup takes 2 seconds), it would require at least 38 hours. However, the team observed an efficient transfer strategy used by elephants to meet this demand:
During this grasping process, the granules undergo multiple transitions between fluid-like and solid-like states under the influence of the trunk. This strategy creates a physical context that adapts dynamically to the nature of the granules, enabling the elephant to capture more material in each action and potentially improve intake efficiency.
Inspired by this bio-informed strategy, the researchers developed a pneumatic soft gripper that utilizes both force-driven and geometry-driven modes to repeatedly induce solid–fluid phase transitions in dispersed granules before completing the grasp.
To achieve successful grasping, the soft gripper must generate sufficient horizontal force, which is dependent on the supplied air pressure. Experimental results showed that increased pressure led to higher success rates. However, excessive pressure introduces two problems:
To balance performance and safety, the team established a minimum pressure database based on achieving a 90% success rate. By inputting the relative size and number of target granules, the corresponding pressure can be selected from the database, enabling the gripper to handle granules of varying sizes and quantities.
To further evaluate the performance of this elephant-trunk-inspired strategy, the team constructed a test scenario for grasping dispersed granules. Experimental results showed that compared to traditional one-by-one grasping strategies, this biologically inspired approach achieved remarkable performance improvements.
Finally, this intelligent manipulation strategy was applied in the food industry, where it was integrated with image recognition technology to enable the efficient transport of granular food items with different sizes and quantities.
This breakthrough in bio-inspired granular manipulation opens new possibilities for automated handling systems across various industries:
Future developments will integrate machine learning algorithms for real-time granule recognition and adaptive pressure control, enabling autonomous operation in dynamic environments with varying granule types and distributions.
Extension of the elephant trunk strategy to handle granules at different scales, from micro-particles in pharmaceutical applications to larger components in manufacturing and assembly processes.
Integration with other bio-inspired manipulation strategies to create comprehensive robotic systems capable of handling diverse material types and shapes in complex industrial environments.
This research demonstrates the power of bio-inspired design in solving complex robotic manipulation challenges. By studying African elephant foraging behavior, we developed an intelligent granular manipulation strategy that achieves 100% success rate with 50% time savings compared to traditional approaches. The pneumatic soft gripper successfully leverages solid-fluid phase transitions in granular materials, opening new possibilities for efficient automated handling systems. Future applications will extend across food processing, pharmaceutical manufacturing, and logistics industries, enabling more efficient and adaptive robotic manipulation systems.
@article{zhao2023transporting,
author = {Zhao, Yuwen and Zhang, Jie and Zhang, Siyuan and Zhang, Peng and Dong, Guixin and Wu, Jianing and Zhang, Jinxiu},
title = {Transporting Dispersed Cylindrical Granules: An Intelligent Strategy Inspired by an Elephant Trunk},
journal = {Advanced Intelligent Systems},
volume = {5},
number = {8},
pages = {2300182},
year = {2023},
}