Linearization-Based Forward Kinematic Algorithm for Tensegrity Structures with Compressible Struts
This paper presents a new local linearization method for elastic forces in tensegrity structures, which can be used to solve forward kinematic problems. Forward kinematic problems are often solved as a part of inverse kinematic algorithms and trajectory p
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Linearization-Based Forward Kinematic Algorithm for Tensegrity Structures with Compressible Struts Sergei Savin , Oleg Balakhnov , and Alexander Maloletov
Abstract This paper presents a new local linearization method for elastic forces in tensegrity structures, which can be used to solve forward kinematic problems. Forward kinematic problems are often solved as a part of inverse kinematic algorithms and trajectory planning in robotics, and it is often desirable to be able to perform those algorithms online. The proposed method allows us to solve forward kinematics as a quadratic program, which makes it fast and reliable and allows us to take advantage of the existing convex programming software. The paper demonstrates the work of the proposed method using a three-link tensegrity structure.
24.1 Introduction Tensegrity structures represent a new class of mechanical systems, consisting of cables (which experience tensile forces), struts (which experience compressive forces), and optionally, bars (which experience both tensile and compressive forces [1]. The systematic study of those structures can be traced back to the works of Snelson, Fuller, and others [2–4]. The interest toward these structures comes from their mechanical properties: good weight-to-stiffness ratio, foldability, resistance to mechanical damage due to collisions [5–8]. Those properties made a subject of space robotics research, where their foldability, relative lightweight, and collision resistance was of value [9]; later tensegrity structures became interesting for collaborative robotics [10], and soft robotics in particular [11]. Particular instances of tensegrity robots include reservoir compliant tensegrity robot (ReCTeR) [5], duct climbing tetrahedral tensegrity (DuCTT) [12], Tetraspine [13], tensegrity quadruped MountainGoat [14], NASA’s SUPERball [15, 16], and TR-3 [9]. Some of those robots had been built and tested, while others exist only as mathematical models and were tested in physics simulation environments. S. Savin (B) · O. Balakhnov · A. Maloletov Innopolis University, 420500 Innopolis, Russia e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 A. Ronzhin and V. Shishlakov (eds.), Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”, Smart Innovation, Systems and Technologies 187, https://doi.org/10.1007/978-981-15-5580-0_24
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As of now, there are a number of separate research directions within the field of tensegrity robotics. One is the use of machine learning (reinforcement learning, evolutionary algorithms, and other tools) for solving form-finding, gait generation, and other problems related to the tensegrity robots [9, 14]. Another direction is gait generation using central pattern generators (CPG) [14, 17], which can be combined with the first approach. There are also attempts to solve classic robotics problems as applied to tensegrity structures: path pla
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