ROFeb 27, 2020

Safe and Compliant Control of Redundant Robots Using Superimposition of Passive Task-Space Controllers

arXiv:2002.12249v20.009 citations
AI Analysis55

This addresses the challenge of robust and safe robot control in shared workspaces, offering an incremental improvement over existing methods by enhancing stability and redundancy utilization.

The paper tackles safe and compliant control of redundant robots in dynamic environments by proposing a superimposition of passive task-space controllers, achieving sub-centimeter tracking performance in demanding tasks without requiring robot dynamics knowledge.

Safe and compliant control of dynamic systems in interaction with the environment, e.g., in shared workspaces, continues to represent a major challenge. Mismatches in the dynamic model of the robots, numerical singularities, and the intrinsic environmental unpredictability are all contributing factors. Online optimization of impedance controllers has recently shown great promise in addressing this challenge, however, their performance is not sufficiently robust to be deployed in challenging environments. This work proposes a compliant control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy. Our control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimization is used). We leverage and introduce a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. Our experimental results demonstrate that the proposed method achieves sub-centimeter tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. he proposed framework achieves such results without knowledge of the robot dynamics and thanks to its passivity is intrinsically stable. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information.

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