MARS (Multiple Autonomous Robot Systems) Research Lab

A Resilient and Adaptable Cooperative Heterogeneous Foraging Robot Swarm

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 cooperation between UGVs and UAVs for completing the foraging task.

Detection and Mitigation of Misleading Pheromone Trails in Foraging Robot Swarms

This study addresses the overlooked aspect of security in swarm robotics by exploring the vulnerabilities of stigmergic communication in foraging robot swarms. More specifically, we study the swarm's susceptibility to attacks that introduce misleading pheromone trails. Simulated scenarios in which detractor robots lay misleading trails to deceive benign foraging robots effectively reduce the raging performance of the swarm. We analyze the impact of the attack on the swarm and evaluate the reduction of foraging efficiency. We proposed a defense strategy using distance-based clustering to isolate detractors efficiently in a simulation. This research highlights the security vulnerabilities in pheromone-based foraging algorithms. Our defense strategy contributed to the development of more resilient foraging algorithms in swarm robotics. [UR2024, ICCCR2024]

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. [ICRA2022, ICRA2021]

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. [ICRA2020, ICRA2019, AutonomousRobots2018]

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