MULTI-OBJECTIVE OPTIMIZATION USING EVOLUTIONARY ALGORITHMS FOR MOBILE ROBOT TRAJECTORY PLANNING

Path planning for mobile robots can be divided into two categories based on how much information is available apriori to planning - global path planning and local path planning. While in global path planning, details about the environment are known to the robot, in local path planning almost all that information is not known in advance. Several methodologies have been proposed for path planning optimization for mobile robots in a known environment. While many of these methods use grid-based and sampling-based algorithms such as A-star (A*) and rapidly exploring random trees (RRT), more recently the application of metaheuristic search-based optimization techniques such as genetic algorithms (GA), particle swarm optimization (PSO) and differential evolutions (DE) have been explored. However, in metaheuristic search-based optimization methods, the fitness function to guide the search is based on rewarding solutions or agents that find the shortest path irrespective of whether the shortest path involves irregular and erratic motion that promotes excessive wear and tear of the robot over time. Papers that have investigated smooth plan planning algorithms almost always use curve smoothing techniques such as higher degree splines (Bezier or B-splines) to construct smooth and continuous trajectories, rather than using kinematics based objective function optimization during path planning. In this paper, we investigate the use of multi-objective kinematics-based performance criteria for global path planning that reward two competing objectives: (1) minimizing twists and turns, and (2) achieving the shortest possible path. The path planning is implemented using four variants of the traditional PSO and DE algorithms. Simulation results are compared with path planning methods using traditional methods based on RRT. Preliminary results show the proposed method converges as quickly as the single objective path planning and provides more realistic and smooth paths (minimizes twists and turns but does not necessarily result in the shortest path). Amongst the algorithms used, the DE and its variants presented in this work perform better than the PSO.