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.

This paper presents a new algorithm for smooth trajectory planning optimization of isotropic translational parallel manipulators (ITPM) that their Jacobian matrices are constant and diagonal over the whole robot workspace. The basic motivation of this work is to formulate the robot kinematic and geometric constraints in terms of optimization variables to reduce the mathematical complexity and running time of the resulting algorithm which are important issues in trajectory planning optimization. To achieve this aim, the end-effector trajectory of ITPMs in Cartesian space is defined using fifth-order B-Splines, and as a main contribution, all of the actuators limitations and robot constraints are formulated in terms of B-Spline parameters with no need of any information about the workspace geometry. Then the total required energy, total time of motion, and maximum absolute value of actuators’ jerk are defined as objective functions and non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve the resulting nonlinear constrained multi-objective optimization problem. Finally, the proposed algorithm is implemented in MATLAB software for Cartesian parallel manipulator (CPM) as a case study, and the results are demonstrated and discussed. The obtained results show the significant performance of the proposed algorithm with no need to evaluate the robot’s constraints and boundaries of its workspace in each point of the end-effector trajectory.