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

TL;DR

This paper introduces a passive, superimposed control framework for redundant robots that ensures safety, stability, and high accuracy in dynamic tasks without requiring detailed robot models or contact information.

Contribution

It proposes a novel hierarchical passive control method with smooth stiffness transitions, improving robustness and safety in complex environments without optimization or model knowledge.

Findings

Achieves sub-centimeter tracking accuracy in dynamic tasks

Maintains safety and robustness during environmental interactions

Operates without requiring robot dynamic models or contact data

Abstract

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.