Kinematic Control of Redundant Robots with Online Handling of Variable Generalized Hard Constraints
This work addresses the need for robust control in unstructured environments for robotics applications, though it is incremental as it builds on the Saturation in the Null Space algorithm.
The authors tackled the problem of controlling redundant robots while simultaneously enforcing hard constraints in both joint and Cartesian spaces, with results demonstrating efficient handling of variable constraints through simulation and experiments.
We present a generalized version of the Saturation in the Null Space (SNS) algorithm for the task control of redundant robots when hard inequality constraints are simultaneously present both in the joint and in the Cartesian space. These hard bounds should never be violated, are treated equally and in a unified way by the algorithm, and may also be varied, inserted or deleted online. When a joint/Cartesian bound saturates, the robot redundancy is exploited to continue fulfilling the primary task. If no feasible solution exists, an optimal scaling procedure is applied to enforce directional consistency with the original task. Simulation and experimental results on different robotic systems demonstrate the efficiency of the approach. The proposed algorithm can be viewed as a generic platform that is easily applicable to any robotic application in which robots operate in an unstructured environment and online handling of joint and Cartesian constraints is critical.