  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.
