Model-Based Control and External Load Estimation of an Extensible Soft Robotic Arm - Chen, Xiaojiao; Duanmu, Dehao; Wang, Zheng (Frontiers in Robotics and AI, 2021)
Soft robotics has widely been known for their compliant characteristics dealing with contraction or manipulation. This soft behavior pattern provides safe and adaptive interactions, greatly relieving the complexity of active control policies. However, another promising aspect of soft robotics, sensing the environment, is not as widely studied. This represents the ability to achieve intelligence information from compliant behavior, which could help to reduce the dependence of sensors, gain a better knowledge of the environment, and enrich high-level control strategies. In this paper, we developed a state-change model of a soft robotic arm, and demonstrate how compliant behavior could be used to sense external load based on this model. Moreover, we propose an improved version of the sensation procedure, further reducing the sensation error. Experiments of both methods are compared and shown, displaying the potential effectiveness of applying these methods.