S^*: A Heuristic Information-Based Approximation Framework for Multi-Goal Path Finding

03/15/2021
by   Kenny Chour, et al.
0

We combine ideas from uni-directional and bi-directional heuristic search, and approximation algorithms for the Traveling Salesman Problem, to develop a novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a 2-approximation guarantee. MGPF aims to find a least-cost path from an origin to a destination such that each node in a given set of goals is visited at least once along the path. We present numerical results to illustrate the advantages of our framework over conventional alternates in terms of the number of expanded nodes and run time.

READ FULL TEXT

page 7

page 10

research
05/09/2022

Informed Steiner Trees: Sampling and Pruning for Multi-Goal Path Finding in High Dimensions

We interleave sampling based motion planning methods with pruning ideas ...
research
04/11/2022

Learning heuristics for A*

Path finding in graphs is one of the most studied classes of problems in...
research
12/18/2019

Busca de melhor caminho entre múltiplas origens e múltiplos destinos em redes complexas que representam cidades

Was investigated in this paper the use of a search strategy in the probl...
research
07/27/2021

Bi-Directional Grid Constrained Stochastic Processes' Link to Multi-Skew Brownian Motion

Bi-Directional Grid Constrained (BGC) stochastic processes (BGCSPs) cons...
research
01/10/2019

On the Distance Between the Rumor Source and Its Optimal Estimate on a Regular Tree

This paper addresses the rumor source identification problem, where the ...
research
01/10/2019

On the Distance Between the Rumor Source and Its Optimal Estimate in a Regular Tree

This paper addresses the rumor source identification problem, where the ...
research
08/15/2023

PKE-RRT: Efficient Multi-Goal Path Finding Algorithm Driven by Multi-Task Learning Model

Multi-goal path finding (MGPF) aims to find a closed and collision-free ...

Please sign up or login with your details

Forgot password? Click here to reset