Comparison and Analysis of Convex Optimization Methods for Potential Field-based Path Planning

Abstract

This paper addresses the problem of the optimization of potential field-based path planning which is a well-known path planning method using attraction fields to goal and repulsive fields from obstacles. The convex optimization methods can be applied to potential field-based path planning to obtain the more efficient path. This paper presents how the convex optimization methods can converge a mobile robot to the goal and analyzes the convergence time according to different environments and methods. The simulation results show that the convex optimization methods were conducted successfully, and the Newton's method was conducted faster than the Gradient descent method.