Performing daily interaction tasks such as opening doors and pulling drawers in unstructured environments is a challenging problem for robots. The emergence of soft-bodied robots brings a new perspective to solving this problem. In this paper, inspired by humans performing interaction tasks through simple behaviors, we propose a hierarchical control system for soft arms, in which the low-level controller achieves motion control of the arm tip, the high-level controller controls the behaviors of the arm based on the low-level controller, and the top-level planner chooses what behaviors should be taken according to tasks. To realize the motion control of the soft arm in interacting with environments, we propose two control methods. The first is a feedback control method based on a simplified Jacobian model utilizing the motion laws of the soft arm that are not affected by environments during interaction. The second is a control method based on [Formula: see text]-learning, in which we present a novel method to increase training data by setting virtual goals. We implement the hierarchical control system on a platform with the Honeycomb Pneumatic Networks Arm (HPN Arm) and validate the effectiveness of this system on a series of typical daily interaction tasks, which demonstrates this proposed hierarchical control system could render the soft arms to perform interaction tasks as simply as humans, without force sensors or accurate models of the environments. This work provides a new direction for the application of soft-bodied arms and offers a new perspective for the physical interactions between robots and environments.
To solve the problem of abnormal angular velocity and angular acceleration in manipulator trajectory motion controlled by quintic spline interpolation algorithm, a manipulator trajectory control algorithm combined with moving average filtering algorithm was proposed. Based on the quintic spline interpolation algorithm, the moving average filtering algorithm was used to clean the abnormal data under the quintic spline interpolation. And the recursive forward dynamics model based on joint space motion was used to design the trajectory motion control of the manipulator. The simulation results show that the manipulator trajectory control algorithm combined with the moving average filtering algorithm has strong constraint ability of diagonal velocity and angular acceleration, and 67% of the maximum velocity and maximum acceleration of the joint axis of the designed manipulator trajectory are significantly reduced, and the curve is smoother.
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