# A Vision-based Scheme for Kinematic Model Construction of Re-configurable Modular Robots

## TL;DR

This paper introduces a vision-based approach using AR tags to automatically identify and construct kinematic models of reconfigurable modular robots, simplifying the process without complex sensors.

## Contribution

The authors propose a novel vision-based method for automatic kinematic model construction of modular robots using AR tags, eliminating the need for expensive sensors or complex detection mechanisms.

## Key findings

- Effective identification of modules and their connections.
- Successful automatic construction of robot kinematic models.
- Validated approach through experimental results.

## Abstract

Re-configurable modular robotic (RMR) systems are advantageous for their reconfigurability and versatility. A new modular robot can be built for a specific task by using modules as building blocks. However, constructing a kinematic model for a newly conceived robot requires significant work. Due to the finite size of module-types, models of all module-types can be built individually and stored in a database beforehand. With this priori knowledge, the model construction process can be automated by detecting the modules and their corresponding interconnections. Previous literature proposed theoretical frameworks for constructing kinematic models of modular robots, assuming that such information was known a priori. While well-devised mechanisms and built-in sensors can be employed to detect these parameters automatically, they significantly complicate the module design and thus are expensive. In this paper, we propose a vision-based method to identify kinematic chains and automatically construct robot models for modular robots. Each module is affixed with augmented reality (AR) tags that are encoded with unique IDs. An image of a modular robot is taken and the detected modules are recognized by querying a database that maintains all module information. The poses of detected modules are used to compute: (i) the connection between modules and (ii) joint angles of joint-modules. Finally, the robot serial-link chain is identified and the kinematic model constructed and visualized. Our experimental results validate the effectiveness of our approach. While implementation with only our RMR is shown, our method can be applied to other RMRs where self-identification is not possible.