Sensing and actuation are intricately connected in soft robotics, where contact may change actuator mechanics and robot behavior. To improve soft robotic control and performance, proprioception and contact sensors are needed to report robot state without altering actuation mechanics or introducing bulky, rigid components. For bioinspired McKibben-style fluidic actuators, prior work in sensing has focused on sensing the strain of the actuator by embedding sensors in the actuator bladder during fabrication, or by adhering sensors to the actuator surface after fabrication. However, material property mismatches between sensors and actuators can impede actuator performance, and many soft sensors available for use with fluidic actuators rely on costly or labor-intensive fabrication methods. Here, we demonstrate a low-cost and easy-to manufacture-tubular liquid metal strain sensor for use with soft actuators that can be used to detect actuator strain and contact between the actuator and external objects. The sensor is flexible, can be fabricated with commercial-off-the-shelf components, and can be easily integrated with existing soft actuators to supplement sensing, regardless of actuator shape or size. Furthermore, the soft tubular strain sensor exhibits low hysteresis and high sensitivity. The approach presented in this work provides a low-cost, soft sensing solution for broad application in soft robotics.
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Locomotion of Linear Actuator Robots Through Kinematic Planning and Nonlinear Optimization
We consider a class of robotic systems composed of high-elongation linear actuators connected at universal joints. We derive the differential kinematics of such robots, and show that any instantaneous velocity of the nodes can be achieved through actuator motions if the graph describing the robot’s configuration is infinitesimally rigid. We formulate physical constraints that constrain the maximum and minimum length of each actuator, the minimum distance between unconnected actuators, the minimum angle between connected actuators, and constraints that ensure the robot avoids singular configurations. We present two planning algorithms that allow a linear actuator robot to locomote. The first algorithm repeatedly solves a nonlinear optimization problem online to move the robot’s center of mass in a desired direction for one time step. This algorithm can be used for an arbitrary linear actuator robot but does not guarantee persistent feasibility. The second method ensures persistent feasibility with a hierarchical coarse-fine planning decomposition, and applies to linear actuator robots with a certain symmetry property. We compare these two planning methods in simulation studies.
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- Award ID(s):
- 1925030
- PAR ID:
- 10195834
- Date Published:
- Journal Name:
- IEEE Transactions on Robotics
- ISSN:
- 1552-3098
- Page Range / eLocation ID:
- 1 to 18
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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