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

• Main Authors: Ebert, Julia T., Berlinger, Florian, Haghighat, Bahar, Nagpal, Radhika
• Format: Conference Proceeding
• Language: English
• Subjects: Behavioral sciences; Benchmark testing; Global communication; Inspection; Robot kinematics; Robot sensing systems; Sensors
• ISSN: 2153-0866
• DOI: 10.1109/IROS47612.2022.9982022

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.