Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

Mahmood Reza Azizi, Alireza Rastegarpanah, Rustam Stolkin

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Abstract

Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.
Original languageEnglish
Article number48
Number of pages27
JournalRobotics
Volume10
Issue number1
Early online date17 Mar 2021
DOIs
Publication statusPublished - Mar 2021

Bibliographical note

Copyright © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Data Access Statement

The software code and the data that support the findings of this study are openly available in Figshare with DOI (doi:10.6084/m9.figshare.13293404), https://figshare.com/s/20d9d42eb384b70ecbdb (accessed on 17 March 2021).

Funding

This research was conducted as part of the project called “Reuse and Recycling of Lithium-Ion Batteries” (RELIB). This work was supported by the Faraday Institution (grant number FIRG005).

Keywords

  • Kane’s dynamics
  • model predictive control
  • obstacle avoidance
  • omnidirectional mobile robot
  • velocity obstacles

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