Excitation Trajectory Optimization for Dynamic Parameter Identification Using Virtual Constraints in Hands-on Robotic System

TL;DR

The paper tackles dynamic parameter identification for co-manipulated robots by designing excitation trajectories that are both collision-free and computationally efficient. It introduces a Gramian-based upper bound $r_c$ on the regression condition number and uses a Fourier-series trajectory parameterization to optimize joint motions under physical constraints, including self-collision avoidance. A minimal inertia parameter set is derived via QR decomposition to enable full-rank regression, with a two-stage optimization and regression pipeline implemented in Python on URDF models through CasADi. Experimental validation on a 7-DOF KUKA LBR Med7 R800 demonstrates faster trajectory generation, competitive condition numbers ($cond(\bar{Y}_b) \approx 51$), and accurate torque prediction, complemented by admittance-control tests in a medical robotics context showing reduced operator workload (NASA-TLX) and successful co-manipulation docking. The work provides practical, safety-focused tooling for dynamic parameter identification in variable tool contexts and offers an open-source framework for broader adoption in industrial and clinical robotics.

Abstract

This paper proposes a novel, more computationally efficient method for optimizing robot excitation trajectories for dynamic parameter identification, emphasizing self-collision avoidance. This addresses the system identification challenges for getting high-quality training data associated with co-manipulated robotic arms that can be equipped with a variety of tools, a common scenario in industrial but also clinical and research contexts. Utilizing the Unified Robotics Description Format (URDF) to implement a symbolic Python implementation of the Recursive Newton-Euler Algorithm (RNEA), the approach aids in dynamically estimating parameters such as inertia using regression analyses on data from real robots. The excitation trajectory was evaluated and achieved on par criteria when compared to state-of-the-art reported results which didn't consider self-collision and tool calibrations. Furthermore, physical Human-Robot Interaction (pHRI) admittance control experiments were conducted in a surgical context to evaluate the derived inverse dynamics model showing a 30.1\% workload reduction by the NASA TLX questionnaire.

Figures (6)

• Figure 1: Snapshots of the trajectory execution for the KUKA LBR Med7 R800 with an EE for different time steps. A1-A7 highlight the individual joints. Our method incorporates the EE's geometry to find trajectories that are collision-free and optimal for dynamic parameter identification.
• Figure 2: Self-collision-free trajectory in joint space (Sec. \ref{['sec:exciting_trajectory_identification_validation']}).
• Figure 3: Self-collision-free trajectory in Cartesian space (Sec. \ref{['sec:exciting_trajectory_identification_validation']}).
• Figure 4: The regression results of the excitation trajectory (Sec. \ref{['sec:exciting_trajectory_identification_validation']}).
• Figure 5: Comparison between actual torques and estimated torques with disturbance by external pHRI forces, refer Sec. \ref{['sec:admittance_control']}.