High-speed electrical connector assembly by structured compliance in a finray-effect gripper

Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, requiring compensation of alignment errors while applying sufficient force in the insertion direction, ideally at high speeds and while grasping a range of components. Vision, tactile, or force sensors can compensate alignment errors, but have limited bandwidth, limiting the safe assembly speed. Passive compliance such as silicone-based fingers can reduce collision forces and grasp a range of components, but often cannot provide the accuracy or assembly forces required. To support high-speed mechanical search and self-aligning insertion, this paper proposes monolithic additively manufactured fingers which realize a moderate, structured compliance directly proximal to the gripped object. The geometry of finray-effect fingers are adapted to add form-closure features and realize a directionally-dependent stiffness at the fingertip, with a high stiffness to apply insertion forces and lower transverse stiffness to support alignment. Design parameters and mechanical properties of the fingers are investigated with FEM and empirical studies, analyzing the stiffness, maximum load, and viscoelastic effects. The fingers realize a remote center of compliance, which is shown to depend on the rib angle, and a directional stiffness ratio of $14-36$. The fingers are applied to a plug insertion task, realizing a tolerance window of $7.5$ mm and approach speeds of $1.3$ m/s.Comment: Under review. arXiv admin note: substantial text overlap with arXiv:2301.0843

Compliant finray-effect gripper for high-speed robotic assembly of electrical components

Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, limiting the speed and robustness of robot assembly, even when using vision, tactile, or force sensors. Connector insertion is a common industrial task, requiring horizontal alignment errors to be compensated with minimal force, then sufficient force to be brought in the insertion direction. The ability to handle a variety of objects, achieve high-speeds, and handle a wide range in object position variation are also desired. Soft grippers can allow the gripping of parts with variation in surface geometry, but often focus on gripping alone and may not be able to bring the assembly forces required. To achieve high-speed connector insertion, this paper proposes monolithic fingers with structured compliance and form-closure features. A finray-effect gripper is adapted to realize structured (i.e. directional) stiffness that allows high-speed mechanical search, self-alignment in insertion, and sufficient assembly force. The design of the finray ribs and fingertips are investigated, with a final design allowing plug insertion with a tolerance window of up to 7.5 mm at high speed.Comment: 8 pages, 3 figures, video here: https://youtu.be/J7EGXtE54oYz, CAD here: https://github.com/richardhartisch/compliantfinra

Differentiable Compliant Contact Primitives for Estimation and Model Predictive Control

Control techniques like MPC can realize contact-rich manipulation which exploits dynamic information, maintaining friction limits and safety constraints. However, contact geometry and dynamics are required to be known. This information is often extracted from CAD, limiting scalability and the ability to handle tasks with varying geometry. To reduce the need for a priori models, we propose a framework for estimating contact models online based on torque and position measurements. To do this, compliant contact models are used, connected in parallel to model multi-point contact and constraints such as a hinge. They are parameterized to be differentiable with respect to all of their parameters (rest position, stiffness, contact location), allowing the coupled robot/environment dynamics to be linearized or efficiently used in gradient-based optimization. These models are then applied for: offline gradient-based parameter fitting, online estimation via an extended Kalman filter, and online gradient-based MPC. The proposed approach is validated on two robots, showing the efficacy of sensorless contact estimation and the effects of online estimation on MPC performance.Comment: Submitted ICRA24. Video available at https://youtu.be/CuCTcmn3H-o Code available at https://gitlab.cc-asp.fraunhofer.de/hanikevi/contact_mp

Model Uncertainty and Robustness for Interactive Robots with Joint Flexibility

Transforming robots from laborers to collaborators promises to significantly broaden their societal impact, but is presently limited by, among other factors, technical feasibility. Interactive robots seek to achieve safe and productive behavior in non-deterministic settings by realizing reactive behavior, which can enable direct human-robot interaction, promising new modalities of power and information flow between human and robots. This can enable assistive or rehabilitative robots which restore or augment human's physical capabilities, as well as expand robotic roles in manufacturing contexts. However, several novel considerations must be made in analysis of robots which seek to achieve interactive behavior. For physical interaction, guarantees of safety become both more important and harder to demonstrate. Often, complex hardware is introduced to meet interactive design criteria (e.g. joint torque sensors which introduce joint flexibility). These more complex dynamics introduce additional sources of model uncertainty, and are typically accompanied by hierarchical controllers which further obfuscate both safety and performance. Additionally, interactive robots will couple with unmodeled environments, changing the effective dynamics of the robot and presenting further analytical challenges.This dissertation examines the safety and performance of uncertain, interactive systems from several perspectives. Limitations to achievable model accuracy and the effects of this model uncertainty on performance and safety are examined analytically and experimentally on series-elastic actuated systems. First, the objectives of interactive robots and constraints introduced by their hardware are introduced. A model for interactive flexible joint robots is motivated which explicitly considers backdriveability of the motor and load-side dynamics. Conditions for passivity of flexible-joint robots which render a load-side impedance are developed, then extended to hold over an uncertain motor model. This robust passivity condition is shown to induce sensible constraints on inner-loop torque controllers. Rigorous means of relaxing this passivity condition are introduced, and the relaxed condition shown to explain interactions known in literature to be unsafe in practice. A model and corresponding uncertainty bound of an experimental setup is characterized through bilateral system identification (i.e. both motor and environment driven) and the results used to validate the robust passivity condition as a practical design tool. This analytical methodology assists hierarchical controller synthesis by generating practical constraints on controller parameters. Performance of a linear interactive system is then defined, and shown to be limited by model uncertainty. However, by incorporating direct measurement of interactive variables (force and motion), this performance can be improved and the robust rendering of desired dynamics can be achieved. A novel controller structure, derived from the disturbance observer, is proposed and analyzed. Again, conditions for passivity are developed, then extended to hold over an uncertain model. For a fixed-structure controller, these conditions are then propagated back onto parameter constraints to inform controller design. The performance of this approach is then validated experimentally.When direct measurement of the interactive force is not feasible, performance improvements can be made by improving model accuracy. Here, a data-driven modeling technique is used to describe non-idealized dynamics. However, interactive systems will couple with unknown environments, making their effective dynamics multimodal and potentially introducing unknown input which confounds identification. A modeling approach suited to these challenges is introduced which allows for the identification of inverse dynamics which are multimodal and subject to intermittent unobserved external disturbances. The passivity of the overall resulting controller policy is shown, and the performance and passivity are validated experimentally.Uncertainty in the environment motivates the need for interactive control, but realization of this control is in turn limited by uncertainty in the robot model. Explicitly exploring the relationship between model uncertainty, safety, and performance can allow relevant limitations to be improved when possible, and respected when not

Robust Passivity and Passivity Relaxation for Impedance Control of Flexible-Joint Robots with Inner-Loop Torque Control

Passivity is a canonical condition for the safety of interactive systems, but practical limitations restrict its utility as a design tool. A system with a passive model can be unstable in high-stiffness environments, passivity is difficult to show with inner-loop controllers, and as it is a binary condition it provides limited design comparison insight; as a result, it is rarely used for inner-loop design. As passivity safety claims are limited by model accuracy, conditions for the passivity of a system with bounded-magnitude model uncertainty (robust passivity) are developed in this paper. Additionally, a condition for coupled environment-robot stability is developed using mixed passivity and small-gain condition, allowing rigorous relaxation of passivity at high frequencies for typical impedance-controlled systems. These approaches are used in the analysis of an impedance-controlled series-elastic actuated system with inner-loop torque control and also compared with traditional design tools (bandwidth ratio, sensitivity function, etc.). The approach is then validated experimentally, identifying model uncertainty bounds under various load conditions, and then using the measured uncertainty for controller synthesis. Robust passivity is then compared with nominal passivity in a validation experiment under manual excitation and impact

Bounded Collision Force by the Sobolev Norm: Compliance and Control for Interactive Robots

A robot making contact with an environment or human presents potential safety risks, including excessive collision force. While experiments on the effect of robot inertia, relative velocity, and interface stiffness on collision are in literature, analytical models for maximum collision force are limited to a simplified mass-spring robot model. This simplified model limits the analysis of control (force/torque, impedance, or admittance) or compliant robots (joint and end-effector compliance). Here, the Sobolev norm is adapted to be a system norm, giving rigorous bounds on the maximum force on a stiffness element in a general dynamic system, allowing the study of collision with more accurate models and feedback control. The Sobolev norm can be found through the H 2 norm of a transformed system, allowing efficient computation, connection with existing control theory, and controller synthesis to minimize collision force. The Sobolev norm is validated, first experimentally with an admittance-controlled robot, then in simulation with a linear flexible-joint robot. It is then used to investigate the impact of control, joint flexibility and end-effector compliance on collision, and a trade-off between collision performance and environmental estimation uncertainty is shown