A constrained control-planning strategy for redundant manipulators
This work addresses constrained motion planning for robotic manipulators, which is an incremental improvement in robotics.
The paper tackles the problem of controlling redundant manipulators under system and environmental constraints by developing an interconnected control-planning strategy that incorporates adaptive control and nullspace methods. Experimental results with a 7 DOF manipulator demonstrate its computational efficiency and real-world performance.
This paper presents an interconnected control-planning strategy for redundant manipulators, subject to system and environmental constraints. The method incorporates low-level control characteristics and high-level planning components into a robust strategy for manipulators acting in complex environments, subject to joint limits. This strategy is formulated using an adaptive control rule, the estimated dynamic model of the robotic system and the nullspace of the linearized constraints. A path is generated that takes into account the capabilities of the platform. The proposed method is computationally efficient, enabling its implementation on a real multi-body robotic system. Through experimental results with a 7 DOF manipulator, we demonstrate the performance of the method in real-world scenarios.