From Network Sensors to Intelligent Systems: A Decade-Long Review of Swarm Robotics Technologies

Area of Science:

• Robotics
• Artificial Intelligence
• Collective Intelligence

Background:

• Swarm Robotics (SR) is inspired by social insects, using simple robots and local rules for complex tasks.
• SR applications include demining, search and rescue, and environmental cleanup.
• Despite advancements, challenges in standardization, scalability, and cost-effective deployment persist.

Purpose of the Study:

• To systematically review Swarm Robotics technologies from 2014-2024.
• To analyze the co-evolution of hardware and software subsystems in SR.
• To identify trends, challenges, and future directions in SR.

Main Methods:

• Conducted a systematic literature review (SLR) of 40 peer-reviewed studies (2014-2024).
• Utilized a defined protocol for study inclusion, exclusion, and data extraction.
• Examined hardware trends (sensors, actuation, communication, energy) and software platforms (meta-heuristics, ROS).

Main Results:

• Hardware and software are interdependent for achieving Swarm Intelligence.
• A significant lack of uniform design standards was observed.
• Pragmatic limitations impede the scalability and deployment of SR systems.

Conclusions:

• SR systems require integrated hardware and software for effective Swarm Intelligence.
• Future SR development should focus on interoperability and energy efficiency.
• Incorporating machine learning (ML) is a promising future direction for SR.