Using Tabu Search to Avoid Concave Obstacles for Source Location

Junqi Zhang, Huan Liu, Peng Zu, Mengshi Zhao, Cheng Wang, Aiiad Albeshri, Abdullah Abusorrah, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Recently, using a particle swarm optimizer (PSO) to guide robots in a source location problem has attracted widespread interest. While being navigated by PSO, robots are easily trapped into U-shape-like concave obstacles such that they move back and forth cyclically and fail to locate a correct source. Existing obstacle avoidance strategies perform well when robots have information about all obstacles. Yet in many real scenes, robots have no prior information. This work proposes a novel PSO based on Tabu Search (PSO-TS) for robots to locate multiple sources. Instead of traditionally setting obstacles as tabu objects, PSO-TS innovatively sets trapping areas as tabu objects such that robots do not need prior knowledge or expensive hardware and much time to obtain obstacle information. The weighted average velocity of a robot is employed to determine if it is stuck inside an obstacle-induced area. If so, a rectangular tabu area is set to push robots out of the area and prevents robots from searching the same area again. The proposed method can be embedded into various source location algorithms to improve their performance. Its obstacle avoidance capability is proved. Finally, experimental results show the algorithmic compatibility, environmental adaptability and obstacle avoidance performance of the proposed method.

Original languageEnglish (US)
Pages (from-to)11720-11732
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number11
StatePublished - Nov 1 2023

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Automotive Engineering
  • Computer Science Applications


  • Source location
  • obstacle avoidance
  • particle swarm optimizer
  • tabu search


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