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Aucone E, Geckeler C, Morra D, Pallottino L, Mintchev S. Synergistic morphology and feedback control for traversal of unknown compliant obstacles with aerial robots. Nat Commun 2024; 15:2646. [PMID: 38531857 DOI: 10.1038/s41467-024-46967-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
Animals traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows to establish and sense contacts anywhere along the shell and facilitates sliding along obstacles. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environment, nor high-level switching conditions for alternating between pushing and sliding. We utilize an optimization-based controller that ensures safety constraints on the robot's state and dampens the oscillations of the environment during interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 N mm rad-1, validates the proposed embodied aerial physical interaction strategy. By also showcasing the traversal of isolated branches, this work makes an initial contribution toward enabling drone flight across cluttered vegetation, with potential applications in environmental monitoring, precision agriculture, and search and rescue.
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Affiliation(s)
- Emanuele Aucone
- Environmental Robotics Laboratory, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.
- Swiss Federal Institute for Forest, Snow and Landscape Research, WSL, Birmensdorf, Switzerland.
| | - Christian Geckeler
- Environmental Robotics Laboratory, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Swiss Federal Institute for Forest, Snow and Landscape Research, WSL, Birmensdorf, Switzerland
| | - Daniele Morra
- Research Center "E. Piaggio", Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Lucia Pallottino
- Research Center "E. Piaggio", Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Stefano Mintchev
- Environmental Robotics Laboratory, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland
- Swiss Federal Institute for Forest, Snow and Landscape Research, WSL, Birmensdorf, Switzerland
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Aucone E, Kirchgeorg S, Valentini A, Pellissier L, Deiner K, Mintchev S. Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring. Sci Robot 2023; 8:eadd5762. [PMID: 36652506 DOI: 10.1126/scirobotics.add5762] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., environmental DNA (eDNA), can be used to monitor biodiversity in a scalable manner if equipped with the appropriate tool. However, the collection of eDNA in terrestrial environments remains a challenge because of the many potential surfaces and sources that need to be surveyed and their limited accessibility. Here, we propose to survey biodiversity by sampling eDNA on the outer branches of tree canopies with an aerial robot. The drone combines a force-sensing cage with a haptic-based control strategy to establish and maintain contact with the upper surface of the branches. Surface eDNA is then collected using an adhesive surface integrated in the cage of the drone. We show that the drone can autonomously land on a variety of branches with stiffnesses between 1 and 103 newton/meter without prior knowledge of their structural stiffness and with robustness to linear and angular misalignments. Validation in the natural environment demonstrates that our method is successful in detecting animal species, including arthropods and vertebrates. Combining robotics with eDNA sampling from a variety of unreachable aboveground substrates can offer a solution for broad-scale monitoring of biodiversity.
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Affiliation(s)
- Emanuele Aucone
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
| | - Steffen Kirchgeorg
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
| | | | - Loïc Pellissier
- Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland.,Ecosystems and Landscape Evolution Group, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland
| | - Kristy Deiner
- Environmental DNA Group, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland
| | - Stefano Mintchev
- Environmental Robotics Laboratory, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Swiss Federal Institute for Forest, Snow, and Landscape Research WSL, Birmensdorf, Switzerland
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Liu K, Ma L, Zhou H, Li S, Zhang K, Huang D, Li B, Zhao D. Optimal Time Trajectory Generation and Tracking Control for Over-Actuated Multirotors With Large-Angle Maneuvering Capability. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3187260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Kun Liu
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Lei Ma
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Hui Zhou
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Shenhang Li
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Kai Zhang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Deqing Huang
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Binbin Li
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
| | - Duo Zhao
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
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Sun Y, Jing Z, Dong P, Huang J, Leung H, Du X. External Wrench Estimation for UAVs Based on Variational Bayesian Unscented Kalman Filter. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3176716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Yinshuai Sun
- School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Zhongliang Jing
- School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Peng Dong
- School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Jianzhe Huang
- School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, P. R. China
| | - Henry Leung
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada
| | - Xin Du
- Aerospace Technology Institute, China Aerodynamics Research and Development Center, Mianyang, P. R. China
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Cuniato E, Lawrance N, Tognon M, Siegwart R. Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3176959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Eugenio Cuniato
- Autonomous Systems Lab (ASL), ETH Zurich, Zurich, Switzerland
| | | | - Marco Tognon
- Autonomous Systems Lab (ASL), ETH Zurich, Zurich, Switzerland
| | - Roland Siegwart
- Autonomous Systems Lab (ASL), ETH Zurich, Zurich, Switzerland
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Ma L, Yan Y, Li Z, Liu J. Neural-embedded learning control for fully-actuated flying platform of aerial manipulation system. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Buzzatto J, Liarokapis M. A Benchmarking Platform and a Control Allocation Method for Improving the Efficiency of Coaxial Rotor Systems. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3153999] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Zhang W, Ott L, Tognon M, Siegwart R. Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3194315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - Lionel Ott
- Autonomous Systems Lab, Zurich, Switzerland
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Bodie K, Tognon M, Siegwart R. Dynamic End Effector Tracking With an Omnidirectional Parallel Aerial Manipulator. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3101864] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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10
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Towards reconfigurable and flexible multirotors. INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2021. [DOI: 10.1007/s41315-021-00200-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Pantic M, Ott L, Cadena C, Siegwart R, Nieto J. Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles. IEEE Robot Autom Lett 2021. [DOI: 10.1109/lra.2021.3061869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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12
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Ollero A, Tognon M, Suarez A, Lee D, Franchi A. Past, Present, and Future of Aerial Robotic Manipulators. IEEE T ROBOT 2021. [DOI: 10.1109/tro.2021.3084395] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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