Our development goal is to develop a mobile manipulator suitable for any design of a test track without human intervention (eg painting, buoyancy). Creating the software ecosystem required for its operation. The mobile platform will be part of an extensible control software ecosystem for ease of use and integrated extensibility. Its main task is to build the mechanical, electronic and control systems of the mobile manipulator. This includes the physical structure of the robot and the first steps of the algorithm development required for operation and control. Development takes place in two branches: the design of the mobile platform and the integration of the robotic arm.

In parallel with the physical developments, we will also create a simulated digital twin, which will greatly facilitate later algorithm developments and tuning.

High-level, open robot programming and Learning Factory case study:

  • The programming environment of autonomous systems is typically vendor-specific, which is an obstacle to the reuse of solutions developed in other environments. However, open source solutions (e.g. ROS - Robot Operating System) are emerging for both industrial robotics and autonomous vehicle systems. The task of the research is to manage several industrial and collaborative robots in a unified programming environment, to map the possibilities and limitations, to coordinate the opposite aspects of safe and autonomous operation.
  • Assessment of case studies implementing the European Learning Factory concept, design of some and at least one specific implementation in the industrial robotics environment available in SZE and in the SZTAKI’s Industry 4.0 sample system with collaborative robots in Győr.

Mobile platform developments:

  • mechanical modeling of a robot platform, following the prototyping process of a robot platform with computer modeling, preliminary computer modeling of development steps;
  • mechanical structure of the platform, design of modular sensor and device receiving levels, installation of drive chain;
  • installation of electronic devices, power supply, on-board computers and basic sensor systems;
  • robot arm integration;
  • designing and finalizing system architecture, identifying appropriate ROS2 components, setting performance requirements;
  • development and testing of a low-level software driver for a mobile platform;
  • installation and integration of a security laser scanner;
  • installation of an embedded target platform (Jetson Nano), selection of necessary components;
  • integration of reference navigation framework (ROS2 navigation2, SLAM toolbox), demonstration of its operation in indoor environment. Development and installation of a general NMEA-based ROS2 driver for GPS positioning sensors;
  • developing its own reconfigurable motion planning architecture;
  • integration of a robotic arm mobile platform into the entire ROS2 ecosystem. User interface development based on Qt and OpenGL;
  • developing route planning algorithms for the autonomous working buoy landing robot;
  • implementing an ecosystem for machine vision, hardware-complemented by an Intel RealSense camera;
  • buoy machine vision based detection.

Robot arm developments:

  • selection of a mobile manipulator suitable for buoyancy, design of its gripper;
  • design and drawing of the first prototype gripper of the manipulator equipped with an electromagnet and an induction sensor with a CAD program;
  • implementing offline and online movement of the manipulator to the desired position in a ROS2 environment;
  • printing and assembling with a gripping 3D printer, testing the operation of the sensor and the electromagnet;
  • integration of a camera that can be mounted on a robot arm, preparation of software development;
  • integrating the manipulator into the software environment as part of the ecosystem.

Simulation improvements:

  • computer simulation of a robot platform and a manipulator placed on it;
  • selection of the simulation environment, configuration according to the requirements of the task, geometric modeling of the robot platform based on a computer model, implementation of its sensor emulation;
  • design of the robot platform interface system using ROS 2 middleware.