Revisiting bounded-suboptimal safe interval path planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Authors
Yakovlev K. 1, 2 , Andreychuk A. 1, 3 , Stern R.4, 5
Conference proceedings
Proceedings International Conference on Automated Planning and Scheduling, ICAPS
Publisher
AAAI press
Language
English
Pages
300-304
Status
Published
Volume
30
Year
2020
Organizations
  • 1 Federal Research Center for Computer Science, Control of Russian Academy of Sciences, Russian Federation
  • 2 National Research University, Higher School of Economics
  • 3 Peoples' Friendship University of Russia, RUDN University, Russian Federation
  • 4 Ben-Gurion University of the Negev, Israel
  • 5 Palo Alto Research Center (PARC), United States
Keywords
Economic and social effects; Scheduling; AS paths; Dynamic obstacles; Optimal solutions; Optimality; Planning time; Safe intervals; Trade off; Robot programming
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