Abstract—In this paper, we have considered optimal motion planning problem of mobile robot. The two hybrid approaches are proposed based on Modified Genetic Algorithm (MGA) and Ant Colony Algorithm (MACO). A sub optimal collision free path is established by proposing the classical search and Modified A* search method (MA*) in initialization stage. The globally optimal path will be finding by optimizing the sub optimal path and then converting it to optimal trajectory. The enhancements for the two approaches are proposed in initialization stage, enhanced operators, and in reducing the energy consumption for mobile robot by using Cubic Spline interpolation curve fitting for optimal planned path. Moreover, four objective optimization functions are proposed for minimizing traveling length, time, smoothness, and security of feasible path and trajectory. By comparing the both approaches, the simulation results demonstrate that the MGA has more accurate and better performance govern the robot’s movements successfully from start to target point after avoiding all obstacles in all tested environment.
 
Index Terms—multi objective optimization, motion planning, mobile robot, genetic algorithm, A* search algorithm, ant colony algorithm

Cite: Bashra K. O. ChaborAlwawi, Hubert Roth, Bahaa I. Kazem, and Mustafa W. Abdullah, "Mobile Robot Motion Planning and Multi Objective Optimization Using Improved Approach," International Journal of Mechanical Engineering and Robotics Research, Vol.4, No. 4, pp. 325-330, October 2015. DOI: 10.18178/ijmerr.4.4.325-330