Optimal Path Planning using Informed Probabilistic Road Map Algorithm

Abstract

This study aims to propose a new path planning algorithm that can guarantee the optimal path solution. The method used is to hybridize the Probabilistic Road Map (PRM) algorithm with the Information Search Algorithm. This hybridization algorithm is called the Informed-PRM algorithm. There are two informed search methods used. The first method is the informed sampling through an ellipsoid subset whose eccentricity is dependent on the length of the shortest current solution that is successfully planned in that iteration. The second method is to use a local search algorithm. The basic PRM algorithm will be run in the first iteration. Since the second iteration, the generation of sample points in the PRM algorithm will be carried out based on information. The informed sampling method will be used to generate 50% of the sampling points. Meanwhile, the remaining number of sample points will be generated using a local search algorithm. Using several benchmark cases, we compared the performance of the Informed-PRM algorithm with the Rapidly Exploring Random Tree* (RRT*) and informed RRT* algorithm. The test results show that the Informed-PRM algorithm successfully constructs the nearly optimal path for all given cases. In producing the path, the time and path cost of the Informed-PRM algorithm is better than the RRT* and Informed RRT* algorithm. The Friedman test was then performed to check for the significant difference in performance between Informed-PRM with RRT* and Informed RRT*. Thus, the Informed-PRM algorithm can be implemented in various systems that require an optimal path planning algorithm, such as in the case of medical robotic surgery or autonomous vehicle systems.

Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm

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Path Planning Using Combined Informed Rapidly- Exploring Random Tree Star and Particle Swarm Optimization Algorithms

“…Path planning is a critical process in many applications [2]. The optimal path planning algorithm seeks to find the optimal path from a start to a goal [3,4]. Path planning algorithms are used in various applications, including autonomous vehicle navigation, medical applications, manufacturing, game development, robotics, and protein folding [5][6][7][8][9][10][11].…”

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

“…Path planning is one of the most researched problems in robotics [1][2][3]. Any path planning algorithm's primary goal is to provide a collision-free path from a start state to an end state within the robot's configuration space [4,5]. The Rapidly Exploring Random Tree (RRT) is a simple and fast algorithm that generates a tree in the configuration space incrementally until the goal is reached [6].…”