Abstract
This paper presents an approach to control the position of a gecko-inspired soft robot in Cartesian space. By formulating constraints under the assumption of constant curvature, the joint space of the robot is reduced in its dimension from nine to two. The remaining two generalized coordinates describe respectively the walking speed and the rotational speed of the robot and define the so-called velocity space. By means of simulations and experimental validation, the direct kinematics of the entire velocity space (mapping in Cartesian task space) is approximated by a bivariate polynomial. Based on this, an optimization problem is formulated that recursively generates the optimal references to reach a given target position in task space. Finally, we show in simulation and experiment that the robot can master arbitrary obstacle courses by making use of this gait pattern generator.
Highlights
Soft robotics is an emerging field in the robotics sciences and enjoys increasing attention in the scientific community (Bao et al, 2018)
The aim of this work was position control of the geckoinspired soft robot from Schiller et al (2019) in Cartesian space. The solution to this complex task is based on two major simplifications: (i) the formulation of a gait law to reduce the state space of the robot from nine to two dimensions and (ii) the approximation of the direct kinematics to allow a fast evaluation
It is possible that the introduction of additional generalized coordinates or a different gait law may lead to a better performance of the robot
Summary
Soft robotics is an emerging field in the robotics sciences and enjoys increasing attention in the scientific community (Bao et al, 2018). As discussed in Santina et al (2017), high gain feedback control results in good tracking performance, but imposes a reduction in the compliance of the controlled system. It takes away the essential characteristic and greatest advantage of a soft robot—its softness (Rus and Tolley, 2015). In order to preserve softness, the feedback gain of the outer control loop needs to be low. As shown in Santina et al (2020), the typical soft properties of a soft robot can be preserved with a model-based feed forward term when doing position control
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
