Distributed Swarm Robotic Systems

Introduction

Distributed swarm robotic systems comprise multiple autonomous robots that cooperate using local sensing, limited communication, and decentralized decision-making. Rather than relying on a central controller, individual robots coordinate their actions through distributed algorithms that enable the group to adapt to changing environments while remaining robust to uncertainty, communication failures, and the loss of individual agents.

Research at CIMLab develops methodologies that enable robotic swarms to perform cooperative sensing and target tracking. A central objective is to establish scalable decision-making strategies that allow large teams of heterogeneous aerial and ground robots to operate efficiently in dynamic environments where information is incomplete, distributed, and continually evolving.

Our work integrates distributed estimation, adaptive task allocation, swarm reconfiguration, cooperative control, and multi-robot coordination into experimentally validated robotic systems. New methodologies are evaluated using custom-built UAV and UGV platforms in laboratory and field environments, providing realistic validation under sensing uncertainty, communication constraints, and dynamic operating conditions.

Representative Research: Collaborative Motion Planning and Control for Distributed Robotic Swarms – Kasra Eshaghi (PhD, 2023)

Autonomous robotic swarms must often operate in environments where GPS is unavailable and sensing and communication are severely limited. This research developed collaborative motion-planning and control methodologies that enable teams of robots to coordinate using only local interactions while maintaining the connectivity required for reliable cooperation. Major contributions included novel open-loop and closed-loop swarm control strategies, together with collaborative motion-planning methods for multi-task missions under communication constraints. mROBerTO 2.0 millirobot The research also led to the design and development of mROBerTO 2.0, a 22 × 20 mm millirobot that provided an experimental platform for validating the proposed algorithms. Collectively, this work advanced the autonomy, robustness, and scalability of distributed robotic swarms operating in challenging real-world environments.

Selected Publications:

Sari, N. N., Sang, Y., Nejat, G., & Benhabib, B. (2026). Reconfigurable Mobile Wireless Sensor Network Coordination for Simultaneous Multi-Target Tracking. Robotics, 15(7), 120.
DOI: 10.3390/robotics15070120
Publisher: https://doi.org/10.3390/robotics15070120

Eshaghi, K., Sari, N. N., Haigh, C., Roman, D., Nejat, G., & Benhabib, B. (2024). Restoring Connectivity in Robotic Swarms – A Probabilistic Approach. Journal of Intelligent & Robotic Systems, 110(2), Article 90.
DOI: 10.1007/s10846-024-02097-0
Publisher: https://doi.org/10.1007/s10846-024-02097-0

Rogers, A., Eshaghi, K., Nejat, G., & Benhabib, B. (2023). Occupancy Grid Mapping via Resource-Constrained Robotic Swarms: A Collaborative Exploration Strategy. Robotics, 12(3), 70.
DOI: 10.3390/robotics12030070
Publisher: https://doi.org/10.3390/robotics12030070

Eshaghi, K., Li, Y., Kashino, Z., Nejat, G., & Benhabib, B. (2020). mROBerTO 2.0 – An Autonomous Millirobot with Enhanced Locomotion for Swarm Robotics. IEEE Robotics and Automation Letters, 5(2), 962–969.
DOI: 10.1109/LRA.2020.2966411
Publisher: https://doi.org/10.1109/LRA.2020.2966411