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

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.

Implementation of Rapidly-Exploring Random Tree (RRT) for Path Planning and Stanley Control Method for Path Tracking on Wheeled Soccer Robots

“…Most MSL teams adopt omniwheels-based drive configurations for the sake of agility [3][4][5][6]. One of the key challenges in robot soccer is motion planning, which refers to the process of determining the optimal path or trajectory that a robot should follow to achieve a specific goal as well as path tracking to control the movement of the robot along the predetermined path [7]. This can be a complicated task, especially in the dynamic and unpredictable environment of a soccer game, where the positions and movements of other robots and the ball are constantly changing [8].…”

Multi-Objective Combinatorial Optimization Using the Cell Mapping Algorithm for Mobile Robots Trajectory Planning

“…This requires navigation systems capable of locating the mobile robot in relative or absolute coordinates over time. Ultimately, the control system is designed to allow the robot to follow the reference trajectory provided by the planner [8][9][10][11][12]. The literature on trajectory planners highlights several open problems, such as generalizing methods for known and partially known or unknown environments; lack of adaptation and non-robust behavior of conventional techniques [13]; high computational costs [14,15]; long routes [16,17]; trajectory generation without considering vehicle kinematic and dynamic constraints [18][19][20][21][22]; and complex environments [23].…”

Path tracking control of differential drive mobile robot based on chaotic-billiards optimization algorithm

“…Ammar and Azar [10] proposed a fractional-order PID (FOPID) for path tracking control of mobile robots. Azizi et al [11] designed a nonlinear model predictive control (NMPC) strategy based on the derived mathematical model for motion planning and control of an omnidirectional mobile robot in dynamic environments. Chen et al [12] proposed a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control where the parameter of KNFC adjusted by knowledge-based cultural multi-strategy differential evolution (KCMDE).…”