A Vision-Based Sensing Approach for a Spherical Soft Robotic Arm

Sensory feedback is essential for the control of soft robotic systems and to enable deployment in a variety of different tasks. Proprioception refers to sensing the robot’s own state and is of crucial importance in order to deploy soft robotic systems outside of laboratory environments, i.e. where no external sensing, such as motion capture systems, is available. A vision-based sensing approach for a soft robotic arm made from fabric is presented, leveraging the high-resolution sensory feedback provided by cameras. No mechanical interaction between the sensor and the soft structure is required and consequently the compliance of the soft system is preserved. The integration of a camera into an inflatable, fabric-based bellow actuator is discussed. Three actuators, each featuring an integrated camera, are used to control the spherical robotic arm and simultaneously provide sensory feedback of the two rotational degrees of freedom. A convolutional neural network architecture predicts the two angles describing the robot’s orientation from the camera images. Ground truth data is provided by a motion capture system during the training phase of the supervised learning approach and its evaluation thereafter. The camera-based sensing approach is able to provide estimates of the orientation in real-time with an accuracy of about one degree. The reliability of the sensing approach is demonstrated by using the sensory feedback to control the orientation of the robotic arm in closed-loop.

Model-Based Control and External Load Estimation of an Extensible Soft Robotic Arm

Soft robotics has widely been known for its compliant characteristics when dealing with contraction or manipulation. These soft behavior patterns provide safe and adaptive interactions, greatly relieving the complexity of active control policies. However, another promising aspect of soft robotics, which is to achieve useful information from compliant behavior, is not widely studied. This characteristic 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 have developed a state-change model of a soft robotic arm, and we demonstrate how compliant behavior could be used to estimate external load based on this model. Moreover, we propose an improved version of the estimation procedure, further reducing the estimation error by compensating the influcence of pressure deadzone. Experiments of both methods are compared, displaying the potential effectiveness of applying these methods.

Optimal Structure and Size of Multi-segment Soft Robotic Arms with Finite Element Method

Pneumatic actuate of multi-segment soft robotic arm is a significant structure and has extensive applications. However, the study of the optimal structure and size of multi-segment soft robotic arm has not been achieved. In this study, the finite element method is used to optimized the structure and size of soft robotic arm. We report that the two-segment structure of soft robotic arm has better performance for the general manipulator operation task through evaluating bending angles with different structures and parameters. The optimal ratio of the total length of non-cavity section to the total length of the soft robotic arm with two-segment is 0.21. And soft robotic arm performs better when the length of the fixed first section, the linkage section between two cavity sections and the end section are equal. Two cavities in each segment has more advantages in tasks of plane bending, while three cavities structure has better adaptability when the task need bend in the space. These results in this study provide a reference and simplify the process for the structure and size design of the multi-segment soft robotic arm in the future.