A Hybrid PSO Algorithm for Multi-robot Target Search and Decision Awareness

Julia Ebert*, Florian Berlinger, Bahar Haghighat, Radhika Nagpal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


Groups of robots can be tasked with identifying a location in an environment where a feature cue is past a threshold, then disseminating this information throughout the group – such as identifying a high-enough elevation location to place a communications tower. This is a continuous-cue target search, where multi-robot search algorithms like particle swarm optimization (PSO) can improve search time through parallelization. However, many robots lack global communication in large spaces, and PSO-based algorithms often fail to consider how robots disseminate target knowledge after a single robot locates it. We present a two-stage hybrid algorithm to solve this task: (1) locating a target with a variation of PSO, and (2) moving to maximize target knowledge across the group. We conducted parameter sweep simulations of up to 32 robots in a grid-based grayscale environment. Pre-decision, we find that PSO with a variable velocity update interval improves target localization. In the post-decision phase, we show that dispersion is the fastest strategy to communicate with all other robots. Our algorithm is also competitive with a coverage sweep benchmark, while requiring significantly less inter-individual coordination.
Original languageEnglish
Title of host publication2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 23-27, 2022, Kyoto, Japan
Number of pages8
ISBN (Print)978-1-6654-7927-1
Publication statusPublished - Oct-2022
Event2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Kyoto, Japan
Duration: 23-Oct-202227-Oct-2022


Conference2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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