Abstract
Pivoting gait is efficient for manipulating a big and heavy object with relatively small manipulating force, in which a robot iteratively tilts the object, rotates it around the vertex, and then puts it down to the floor. However, pivoting gait can easily fail even with a small external disturbance due to its instability in nature. To cope with this problem, we propose a controller to robustly control the object motion during the pivoting gait by introducing two gait modes, i.e., one is the double-support mode, which can manipulate a relatively light object with faster speed, and the other is the quadruple-support mode, which can manipulate a relatively heavy object with lower speed. To control the pivoting gait, a graph model predictive control is applied taking into account of these two gait modes. By adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even if the external disturbance is applied to the object.
Highlights
Currently used robots mostly manipulate objects by picking them up once [1]–[3], a pick-and-place manipulation is energy consuming and is not adequate for manipulating a large and heavy object, since the grasped object has to be completely lifted
EXPERIMENT 2: UNCERTAINTY IN THE OBJECT’s MASS Though the model predictive controller (MPC) is inherently able to cope with external disturbances, we believe selecting the proper gait mode improves the robustness of the controller
Two gait modes are designed for adapting to the environment: double support (DS) mode is for fast walking, and quadruple support (QS) mode is for stable walking
Summary
Currently used robots mostly manipulate objects by picking them up once [1]–[3], a pick-and-place manipulation is energy consuming and is not adequate for manipulating a large and heavy object, since the grasped object has to be completely lifted. The formulation of a pivoting gait is more complex than that of a biped gait since a force-controlled dual-arm manipulator controls the contact mode of the grasped object. This research applies the MPC to the pivoting gait in which a dampingcontrolled dual-arm manipulator is controlled to change the gait mode while predicting the object’s future dynamics. A graph MPC is proposed to select the proper gait modes and to realize feedback control by using the vision and force information. FORMULATION OF MODEL PREDICTIVE CONTROL The target system of this work consists of two robot arms and a rigid object with a polygonal shape; see Fig. 2
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