In this paper, we present a gecko-inspired soft robot that is able to climb inclined, flat surfaces. By changing the design of the previous version, the energy consumption of the robot could be reduced, and at the same time, its ability to climb and its speed of movement could be increased. As a result, the new prototype consumes only about a third of the energy of the previous version and manages to climb slopes of up to 84 •. In the horizontal plane, its velocity could be increased from 2 to 6 cm/s. We also provide a detailed analysis of the robot's straight gait.
Soft robotics is an emerging field in the robotics community which deals with completely new types of robots. However, often new soft robotic designs depend on the ingenuity of the engineer rather being systematically derived. For this reason, in order to support the engineer in the design process, we present a design methodology for general technical systems in this paper and explain it in depth in the context of soft robotics. The design methodology consists of a combination of state-of-the-art engineering concepts that are arranged in such a way that the engineer is guided through the design process. The effectiveness of a systematic approach in soft robotics is illustrated on the design of a new gecko-inspired, climbing soft robot.Electronic supplementary materialThe online version of this article (10.1186/s40638-018-0088-4) contains supplementary material, which is available to authorized users.
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.
We introduce a sensor concept for an integrated measurement of the curvature angle of soft bending actuators using inertial measurement units (IMUs). In particular, IMUs are placed at both ends of the soft bending actuator, and the integrated magnetic sensors are used for small and the integrated acceleration sensors for medium and large inclination angles of the soft actuator’s bending plane. The experimental results show absolute measurement errors of up to 20° for small and less than 5° for medium and large inclination angles. Furthermore, we investigate experimentally whether the assumption of a constant curvature in our sensor concept is still fulfilled when the soft bending actuator is loaded by an external force at its free end. The results indicate that this is the case for loading masses of up to 30 g at large inclination angles.
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