MARS (Multiple Autonomous Robot Systems) Research Lab 

A Resilient and Adaptable Cooperative Heterogeneous Foraging Robot Swarm

Javier Becerril, Arturo Gonzalez

We plan to develop a heterogeneous foraging robot swarm. It is relatively difficult for unmanned ground vehicles/robots (UGVs) to search for resources in a large environment. UAVs have better sensing and further vision that complement the ground robot swarms. Therefore, the collaboration between the UGVs and UAVs has the potential to improve the foraging performance significantly. We will demonstrate the efficient collaboration between UGVs and UAVs for completing the foraging task. 

Epidemiology-Based Models for Self-Propagating Cyber Attacks and Defenses in Foraging Swarm Robotics

Ryan Luna

We will investigate the potential security problems and threats in the swarm robotic systems. We will apply the compartmental (epidemiology-based) models to study malware self-propagation in robotic swarms. The compartmental models have been widely used to analyze and forecast the Ebola virus and the spread of the Coronavirus (COVID-19) recently. There has been extensive research in customizing compartmental models to study the behavior of malware propagation in general computer networks. We believe this to be one of the first ongoing research efforts on its application towards swarm robotics. We will disclose the dynamics of malware self-propagation in swarm robotic systems and how it impacts the foraging performance of robot swarms. It will instruct administrators to take suitable security and quarantine strategies or design efficient intrusion detection systems to suppress malware propagation in swarm robotic systems. 

Nanoscale Foraging and Self-Assembly Swarms

This project will explore fundamental theoretical questions in the intersection of two critical areas of swarm robotics: foraging swarms and nanoscale self-assembly robots. We propose to explore the new direction of designing foraging algorithms for nanoscale robot swarms -- an area that we have not studied yet and has important implications. We will model the concrete mathematical and computational foraging problems in nanoscale self-assembly robots. One of the challenges is the limitation of the robot size in the nanoscale. We will find the solution to the delivery of resources based on some graph theories in computational geometry. 

Dynamic Robot Chain Networks

Dohee Lee, Qi Lu and Tsz-Chiu Au

We propose a novel extension to the multiple-place foraging in which multiple robot chains are deployed dynamically. Each robot chain connects a foraging location to the central collection zone. Instead of delivering resources by a single robot, resources are passed on robot chains from foraging locations to the center directly such that congestion near the central collection zone can be avoided.  Dynamic robot chains can also relocate themselves to get closer to the resources while avoiding obstacles.  We simulate our robot swarms in the robot simulator ARGoS. Our experiments show that robots with dynamic chains outperform our previous work in robots with dynamic depots and have less congestion.  


Foraging Robot Swarms

Qi Lu, Antonio D. Griego, Takaya Tsuno, Joshua P. Hecker, G. Matthew Fricke, and Melanie E. Moses

We design algorithms for coordinating multiple robots to accomplish a task collectively. For example, foraging is the behavior of social insects (e.g., ant colonies, and honey bees) of searching for foods and transporting them to their nests. We design robots to mimic the foraging behavior for searching for certain resources (e.g., minerals, hazardous waste, and survivors) in a largely unknown area and transporting them to specific locations (e.g., warehouses, hospitals, or military bases). The foraging task is a useful abstraction of many complex, real-world applications such as humanitarian de-mining, search and rescue operations, intrusion tracking, agricultural harvesting, infrastructure inspection, and planetary exploration. 

Current students:

Master's Students

Undergraduate Students

Past students:

Ph.D. students

Master's Students  

Undergraduate Students


Recent Publications

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Patent