Naturally inspired optimization algorithms as applied to mobile robotic path planning

Abstract

Global path planning as applied to mobile robotics can be approached in a similar fashion as classic optimization problems involving combinational constraints (e.g. the Traveling Salesman Problem). A single, exact optimal solution for the shortest path may not exist, and obtaining near-optimal solutions selected and ranked by criteria, or deemed “good-enough”, can satisfy the problem. An overview is provided on a select subset of naturally inspired iterative search algorithms; Simulated Annealing (SA), Genetic Algorithm (GA), and Ant Colony Optimization (ACO) have all been studied and applied to the task of mobile robotic path planning. These three techniques or algorithms (respectively) represent a broader range of naturally inspired physical processes, evolutionary or biological processes, and animal kingdom behavioral examples. It has been demonstrated that these algorithms have been utilized on their own, or as part of a collaborative hybridization of iterative algorithms and heuristic modifiers, to effectively balance the constraints, strengths and weaknesses in a given path planning approach. A brief contextual summary of current literature provides insights regarding implementation of this category of algorithms, and suggests approaches for future experimentation and research in this topic area.