Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (2): 668-682.doi: 10.1007/s42235-022-00291-0
Complementary Methods to Acquire the Kinematics of Swimming Snakes: A Basis to Design Bio-inspired Robots
Elie Gautreau1; Xavier Bonnet2; Tom Fox2; Guillaume Fosseries2; Valéry Valle1; Anthony Herrel3; Med Amine Laribi1
- 1 Department of GMSC, University of Poitiers, ISAEENSMA, CNRS, PPRIME UPR 3346, Poitiers, France 2 CEBC Center for Biological Studies of Chizé, CNRS and University of la Rochelle, Villiers en Bois, France 3 MNHN National Museum of Natural History, CNRS, Paris, France
Complementary Methods to Acquire the Kinematics of Swimming Snakes: A Basis to Design Bio-inspired Robots
Elie Gautreau1; Xavier Bonnet2; Tom Fox2; Guillaume Fosseries2; Valéry Valle1; Anthony Herrel3; Med Amine Laribi1
- 1 Department of GMSC, University of Poitiers, ISAEENSMA, CNRS, PPRIME UPR 3346, Poitiers, France 2 CEBC Center for Biological Studies of Chizé, CNRS and University of la Rochelle, Villiers en Bois, France 3 MNHN National Museum of Natural History, CNRS, Paris, France
摘要： The vast diversity of morphologies, body size, and lifestyles of snakes represents an important source of information that can be used to derive bio-inspired robots through a biology-push and pull process. An understanding of the detailed kinematics of swimming snakes is a fundamental prerequisite to conceive and design bio-inspired aquatic snake robots. However, only limited information is available on the kinematics of swimming snake. Fast and accurate methods are needed to fill this knowledge gap. In the present paper, three existing methods were compared to test their capacity to characterize the kinematics of swimming snakes. (1) Marker tracking (Deftac), (2) Markerless pose estimation (DeepLabCut), and (3) Motion capture were considered. (4) We also designed and tested an automatic video processing method. All methods provided different albeit complementary data sets; they also involved different technical issues in terms of experimental conditions, snake manipulation, or processing resources. Marker tracking provided accurate data that can be used to calibrate other methods. Motion capture posed technical difficulties but can provide limited 3D data. Markerless pose estimation required deep learning (thus time) but was efficient to extract the data under various experimental conditions. Finally, automatic video processing was particularly efficient to extract a wide range of data useful for both biology and robotics but required a specific experimental setting.