Global path planning and navigation of an omnidirectional Mecanum mobile robot

Abstract

This paper develops techniques and methodologies for global path planning and navigation of a Mecanum-wheeled omnidirectional mobile robot (MWOMR). The proposed navigation system is composed of three modules: odometry, nonsingular terminal sliding-mode (NTSM) dynamic motion controller, and global path planner, which have been implemented using the SoPC technology. The odometry is constructed by using a numerical method and a kinematic model of the robot, in order to keep track of the current position and orientation of the robot over short distances. A nonsingular terminal sliding-mode dynamic controller is well derived to achieve simultaneous point stabilization and trajectory tracking. A hybrid PSO (particle swarm optimization)-RGA (real-coded genetic algorithm) algorithm is proposed to find an optimal path between a starting and ending point in a given grid environment. Simulations and experimental results are conducted which have shown the feasibility and effectiveness of the proposed global path planning and navigation methods.

Analysis of Traveling Strategies for Driving Omni-Wheeled Vehicle Around a Corner

“…There have been studies not only on the motion analysis and modeling of the omnidirectional vehicles but also on their motion planning. Various approaches concerning the motion planning were proposed, such as the dynamic inversion-based scheme for the real-time trajectory generation [17], the global path planning system composed of the odometry, motion controller, and global path planner [18], and the local reactive approach for avoiding the moving obstacles [19]. These studies aim to search for the time-optimal trajectory when vehicles with omnidirectional mobility need to avoid obstacles.…”

Energy Modeling and Experimental Validation of Four-Wheel Mecanum Mobile Robots for Energy-Optimal Motion Control

“…The corresponding energy consumption is different when traveling through different trajectories under different control methods. Despite many studies on obstacle avoidance and optimal path planning [15,16], it must be pointed out that the optimal path is not necessarily the path with the least energy consumption for FWMRs. In general, shorter paths will result in reduced energy consumption.…”

Annealing-based guidance of a rescue robot for rescue mission with multi-goal navigation

“…Path planning and map building is one of the issues in the field of robotics that attempts to find and optimize the path from the initial position to the final position while the local map is dynamically built up as the robot moves. There have been a large number of studies on navigation and building of an autonomous mobile robot by a variety of methodologies such as Genetic algorithms ( [1,7]), electric charge method ( [2]), map-based method ( [3]), neural networks model ( [4][5]), rapidly exploring random tree ([6]), particle swarm optimization ( [7]), ant colony optimization ( [8]). Tsai et al [1] developed parallel elite genetic algorithms model for global path planning, in which a near-optimal collision-free continuous route is generated in structured workspace.…”

“…Tsai et al [7] suggested a hybrid motion planning system by integrating PSO (particle swarm optimization) and RGA (real-coded genetic algorithm) algorithm. A numerical approach and a kinematic model of the robot are explored to construct odometry.…”