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OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment

Siqi Tan, Xiaoya Zhang, Jingyao Li, Ruitao Jing, Mufan Zhao, Yang Liu, Quan Quan

TL;DR

Results show that OA-Bug can improve the performance of swarm robots in a denied environment and is significantly improved compared with a similar algorithm, SGBA.

Abstract

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSm9eM.

OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment

TL;DR

Results show that OA-Bug can improve the performance of swarm robots in a denied environment and is significantly improved compared with a similar algorithm, SGBA.

Abstract

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSm9eM.
Paper Structure (13 sections, 6 figures, 2 tables)

This paper contains 13 sections, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Problem Statement and Algorithm Formulation
  3. Problem Statement
  4. Algorithm Formulation
  5. Software Simulation
  6. Simulation Environment
  7. Simulation Design
  8. Simulation Results and Discussions
  9. Real-World Experimental Results
  10. Hardware Setup and Testing
  11. Experimental Design
  12. Experimental Results and Discussions
  13. Conclusion and Future Work

Figures (6)

  • Figure 1: Swarm Robots with Olfactory and Auditory
  • Figure 2: Site Definitions and Descriptions
  • Figure 3: The Finite State Machine of OA-Bug
  • Figure 4: Simulation Results of Searching (Note that the average coverage associated with total action count is relatively lower than the coverage shown elsewhere, because if a run has achieved 100% coverage before the maximum action limit is reached, the simulation and recording ends early.)
  • Figure 5: Sample Result Screenshots of Different Algorithms (*Not yet fully ported to our setting. Just a general illustration based on FSM in c1.)
  • ...and 1 more figures