@conference{Starks2023,
title = {HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With Open-Source ROS Support},
author = {Michael Starks and Aryan Gupta and Sanjay Sarma O V and Ramviyas Parasuraman},
url = {https://ieeexplore.ieee.org/document/10039174},
doi = {10.1109/SII55687.2023.10039174},
year = {2023},
date = {2023-01-23},
booktitle = {2023 IEEE/SICE International Symposium on System Integrations (SII 2023) },
publisher = {IEEE},
abstract = {Experiments using large numbers of miniature swarm robots are desirable to teach, study, and test multi-robot and swarm intelligence algorithms and their applications. To realize the full potential of a swarm robot, it should be capable of not only motion but also sensing, computing, communication, and power management modules with multiple options. Current swarm robot platforms developed for commercial and academic research purposes lack several of these important attributes by focusing on one of these aspects. Therefore, in this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform, with open-source hardware and software support. The proposed robot hardware is a low-cost design with commercial off-the-shelf components that uniquely integrates multiple sensing, communication, and computing modalities with various power management capabilities into a tiny footprint. Moreover, our swarm robot with odometry capability with Robot Operating Systems (ROS) support is unique in its kind. This simple, yet powerful swarm robot design has been extensively verified with different prototyping variants and multi-robot experimental demonstrations.},
keywords = {control, multi-robot, swarm-robotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Gui2022,
title = {Message Expiration-Based Distributed Multi-Robot Task Management},
author = {Yikang Gui and Ehsan Latif and Ramviyas Parasuraman},
year = {2022},
date = {2022-01-27},
booktitle = {The 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2022)},
abstract = {Distributed task assignment for multiple agents raises fundamental and novel control theory and robotics problems. A new challenge is the development of distributed algorithms that dynamically assign tasks to multiple agents, not relying on prior assignment information. This work presents a distributed method for multi-robot task management based on a message expiration-based validation approach. Our approach handles the conflicts caused by a disconnection in the distributed multi-robot system by using distance-based and timestamp-based measurements to validate the task allocation for each robot. Simulation experiments in the Robotarium simulator platform have verified the validity of the proposed approach.},
keywords = {multi-robot, planning, swarm-robotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Sarma2022,
title = {A study on the ephemeral nature of knowledge shared between multi-agent and swarm systems through behavior trees},
author = {Sanjay Sarma and Ramviyas Parasuraman and Ramana Pidaparti},
year = {2022},
date = {2022-01-27},
booktitle = {The 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2022)},
abstract = {Achieving knowledge sharing within an artificial swarm system could lead to significant development in autonomous multiagent and robotic systems research and realize collective intelligence. However, this is difficult to achieve since there is no generic framework to transfer skills between agents other than a query-response-based approach. Moreover, natural living systems have a "forgetfulness" property for everything they learn. Analyzing such ephemeral nature (temporal memory properties of new knowledge gained) in artificial systems has never been studied in the literature. We propose a behavior tree-based framework to realize a query-response mechanism for transferring skills encoded as the condition-action control subflow of that portion of the knowledge between agents to fill this gap. We simulate a multiagent group with different initial knowledge on a foraging mission. While performing basic operations, each robot queries other robots to respond to an unknown condition. The responding robot shares the control actions by sharing a portion of the behavior tree that addresses the queries. Specifically, we investigate the ephemeral nature of the new knowledge gained through such a framework, where the knowledge gained by the agent is either limited due to memory or is forgotten over time. Our investigations show that knowledge grows proportionally with the duration of remembrance, which is trivial. However, we found minimal impact in knowledge growth due to memory. We compare these cases against a baseline that involved full knowledge precoded on all agents. We found that the knowledge-sharing strived to match the baseline condition by sharing and achieving knowledge growth as a collective system. },
keywords = {behavior-trees, multiagent-systems, swarm-robotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Latif2022,
title = {Energy-Aware Multi-Robot Task Allocation in Persistent Tasks},
author = {Ehsan Latif and Yikang Gui and Aiman Munir and Ramviyas Parasuraman},
year = {2022},
date = {2022-01-27},
booktitle = {The 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2022)},
journal = {The 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics},
abstract = {The applicability of the swarm robots to perform foraging tasks is inspired by their compact size and cost. A considerable amount of energy is required to perform such tasks, especially if the tasks are continuous and/or repetitive. Real-world situations in which robots perform tasks continuously while staying alive (survivability) and maximizing production (performance) require energy awareness. This paper proposes an energy-conscious distributed task allocation algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative robots to achieve highly effective missions. We consider efficiency as a function of the energy consumed by the robot during exploration and collection when food is returned to the collection bin. Finally, the proposed energy-efficient algorithm minimizes the total transit time to the charging station and time consumed while recharging and maximizes the robot's lifetime to perform maximum tasks to enhance the overall efficiency of collaborative robots. We evaluated the proposed solution against a typical greedy benchmarking strategy (assigning the closest collection bin to the available robot and recharging the robot at maximum) for efficiency and performance in various scenarios. The proposed approach significantly improved performance and efficiency over the baseline approach.},
keywords = {multi-robot, planning, swarm-robotics},
pubstate = {published},
tppubtype = {conference}
}