# Inertial Parameter Identification in Robotics: A Survey
## 11 Conclusions and Future Works  
In this paper, a sample of the most popular approaches to inertial parameter identification for fixed-based robotic systems is evaluated and benchmarked. These methods are IDIM-OLS, IDIM-WLS, IDIM-IRLS, IDIM-TLS, ML, IDIM-IV, DIDIM, CLIE, CLOE, EKF, SREKF, UKF, SRUKF, CDKF, SRCDKF, AdaNN, HTRNN, PC-IDIM-OLS, PC-IDIM-WLS, PC-IDIM-IRLS, PC-DIDIM and PC-IDIM-IV. First presented and discussed in a theoretical manner, each method is then implemented and evaluated experimentally within a dedicated framework, named BIRDy, which was specifically developed for this purpose. BIRDy features a complete identification pipeline, allowing one to generate the kinematic and dynamic models of a given robot, to compute a trajectory that excites its dynamic parameters, to simulate the system's behavior along this trajectory, collect experimental data under well-defined conditions, proceed to parameter identification using a pool of dedicated algorithms and eventually to compare the identification performances using a set of suitable metrics. In this work, we used BIRDy to perform Monte Carlo simulations on two models of 6-DoF industrial robot manipulators, namely the Staubli TX40 and the Mitsubishi RV2 SQ. Experiments were also carried out on the real robots, thereby providing helpful insight on the influence of multiple factors, including prior knowledge of the control architecture, sampling frequency, or friction model. The results allow us to provide a set of general guidelines based on quantitative arguments regarding the applicability of a given identification method to a particular experimental context. Future works will mainly consist of extending our study to physically consistent parameter identification algorithms and, in the long run, to the context of parallel and floating-base robots such as humanoids. Multiple improvements of BIRDy are currently being discussed in order to address these issues.  
This work has been supported by BayFrance (Franco-Bavarian University cooperation center) under the grant number FK20_2019.  
Not applicable.  
The complete benchmark code as well as the raw experiment data are made available to the reader at the following addresses (accessed on the 9 May 2021):  
* BIRDy source code: [[https://github.com/TUM-ICS/BIRDy](https://github.com/TUM-ICS/BIRDy)]([https://github.com/TUM-ICS/BIRDy](https://github.com/TUM-ICS/BIRDy))
* Experimental data: [[http://doi.org/10.5281/zenodo.4728085](http://doi.org/10.5281/zenodo.4728085)]([http://doi.org/10.5281/zenodo.4728085](http://doi.org/10.5281/zenodo.4728085))
* Experimental results: [[http://doi.org/10.5281/zenodo.4679467](http://doi.org/10.5281/zenodo.4679467)]([http://doi.org/10.5281/zenodo.4679467](http://doi.org/10.5281/zenodo.4679467))  
The authors would like to thank [PERSON] as well as [PERSON] for their valuable assistance during the experiments.  
Figure 11: Decision process for parameter identification algorithm selection. Physical consistency of the estimates can be enforced using SDP within the algorithms displayed in red. Methods in **bold** caption are, or can potentially be formulated in a recursive manner which makes them capable of running in a real-time control loop.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.