Improving Solution Quality for Experience-Based Framework Through Clustering Algorithms

Mustafa F. Abdelwahed,Mohamed Aly Saleh,Amr E. Mohamed

doi:10.1109/access.2019.2932893

Mustafa F. Abdelwahed, Mohamed Aly Saleh + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2932893

Copy DOI

Abstract

This paper presents an extension for the current developed experience-based frameworks. The current experience-based scheme depends on executing two parallel threads; one tries to solve the problem using traditional approaches, while the other thread uses experience from past solutions for solving it. Once one of these threads solves the problem, its solution is promoted to be the problem's solution, yet disregarding the solution quality. However, our extension to experience-based frameworks uses clustering to decides which thread will solve the problem while maintaining solution quality. We have used experience-based motion planners for benchmarking our approach, where the presented results demonstrate that this approach works with different experience representations while maintaining better path quality, experience utilization, and reduced computational cost.

Highlights

The motion planning problem is defined as the problem of finding an optimal collision-free path reaching a predefined goal while satisfying a set of constraints since optimality is user-determined
Communities like robotics, control, and AI gave much attention to this problem [1]–[3] since motion planning is an essential task for any autonomous vehicles. [4] showed that the optimal path planning is a PSPACE-hard problem, and [5] abstracted the motion planning problems into a class of problems called spatial planning, in a unified way using the notation of configuration space C
Afterwards, [7] proved that exact algorithms with practical computational complexity are unavailable for shortest path planning in arbitrary environments, The associate editor coordinating the review of this manuscript and approving it for publication was Yangming Li

Summary

Introduction

The motion planning problem is defined as the problem of finding an optimal collision-free path reaching a predefined goal while satisfying a set of constraints since optimality is user-determined (e.g., shortest path, minimum path clearance, or mechanical work). Reference [6] proposed the following terms: probabilistically complete and asymptotically optimal. In addition to probabilistically complete, which is defined to describe algorithms that find a solution, if one exists, with probability approaching one while increasing computation time and excellent visibility properties. Optimal is used for algorithms that converge to an optimal solution with probability one. Afterwards, [7] proved that exact algorithms with practical computational complexity are unavailable for shortest path planning in arbitrary environments, The associate editor coordinating the review of this manuscript and approving it for publication was Yangming Li

Methods

Discussion

Conclusion