@conference{Gąsieniec2025,
title = {Brief Announcement: Anonymous Distributed Localisation via Spatial Population Protocols},
author = { Leszek Gąsieniec, Łukasz Kuszner, Ehsan Latif, Ramviyas Parasuraman, Paul Spirakis, Grzegorz Stachowiak},
url = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAND.2025.19},
doi = {10.4230/LIPIcs.SAND.2025.19},
year = {2025},
date = {2025-06-02},
booktitle = {4th Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2025)},
abstract = {In the distributed localization problem (DLP), n anonymous robots (agents) A₀, …, A_{n-1} begin at arbitrary positions p₀, …, p_{n-1} ∈ S, where S is a Euclidean space. Initially, each agent A_i operates within its own coordinate system in S, which may be inconsistent with those of other agents. The primary goal in DLP is for agents to reach a consensus on a unified coordinate system that accurately reflects the relative positions of all points, p₀, …, p_{n-1}, in S. Extensive research on DLP has primarily focused on the feasibility and complexity of achieving consensus when agents have limited access to inter-agent distances, often due to missing or imprecise data. In this paper, however, we examine a minimalist, computationally efficient model of distributed computing in which agents have access to all pairwise distances, if needed. Specifically, we introduce a novel variant of population protocols, referred to as the spatial population protocols model. In this variant each agent can memorise one or a fixed number of coordinates, and when agents A_i and A_j interact, they can not only exchange their current knowledge but also either determine the distance d_{ij} between them in S (distance query model) or obtain the vector v→_{ij} spanning points p_i and p_j (vector query model). We present here a leader-based localisation protocol with distance queries.
},
keywords = {multi-robot systems, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Tahir2024,
title = {Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles},
author = {Nazish Tahir, Ramviyas Parasuraman, Haijian Sun},
url = {https://ieeexplore.ieee.org/abstract/document/10757784},
doi = {10.1109/VTC2024-Fall63153.2024.10757784},
year = {2024},
date = {2024-10-10},
booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
pages = {2577-2465},
abstract = {Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency.
},
keywords = {computing, multi-robot systems, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Munir2024c,
title = {Route Planning for Electric Vehicles with Charging Constraints},
author = {Aiman Munir, Ramviyas Parasuraman, Jin Ye, WenZhan Song},
url = {https://ieeexplore.ieee.org/abstract/document/10757558},
doi = {10.1109/VTC2024-Fall63153.2024.10757558},
year = {2024},
date = {2024-10-10},
booktitle = {2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},
pages = {2577-2465},
abstract = {Recent studies demonstrate the efficacy of machine learning algorithms for learning strategies to solve combinatorial optimization problems. This study presents a novel solution to address the Electric Vehicle Routing Problem with Time Windows (EVRPTW), leveraging deep reinforcement learning (DRL) techniques. Existing DRL approaches frequently encounter challenges when addressing the EVRPTW problem: RNN-based decoders struggle with capturing long-term dependencies, while DDQN models exhibit limited generalization across various problem sizes. To overcome these limitations, we introduce a transformer-based model with a heterogeneous attention mechanism. Transformers excel at capturing long-term dependencies and demonstrate superior generalization across diverse problem instances. We validate the efficacy of our proposed approach through comparative analysis against two state-of-the-art solutions for EVRPTW. The results demonstrated the efficacy of the proposed model in minimizing the distance traveled and robust generalization across varying problem sizes.
},
keywords = {control, learning, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Latif2023,
title = {Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration},
author = {Ehsan Latif and WenZhan Song and Ramviyas Parasuraman},
doi = {10.1109/INFOCOMWKSHPS57453.2023.10226167},
year = {2023},
date = {2023-05-01},
booktitle = {IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)},
abstract = {Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for coordinating swarm robots. It is made efficient by hierarchically building on the use of local information exchanges. We consider a case study application of maze solving through cooperation among a group of robots, where the time and costs are minimized while avoiding inter-robot collisions and path overlaps during exploration. With a solid theoretical basis, we extensively analyze the algorithm with realistic CORE network simulations and evaluate it against state-of-the-art solutions in terms of maze coverage percentage and efficiency under communication-degraded environments. The results demonstrate significantly higher coverage accuracy and efficiency while reducing costs and overlaps even in high packet loss and low communication range scenarios.},
keywords = {cooperation, mapping, multi-robot, multi-robot systems, networking},
pubstate = {published},
tppubtype = {conference}
}

@conference{Latif2022c,
title = {DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems},
author = {Ehsan Latif and Ramviyas Parasuraman},
year = {2022},
date = {2022-11-30},
booktitle = {The 16th International Symposium on Distributed Autonomous Robotic Systems (DARS) 2022},
abstract = {An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g$^2$o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations. The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.},
note = {Nominated for the Best Student Paper Award},
keywords = {localization, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}

@workshop{Tahir2022,
title = {Collaborative Control of Mobile Robots Using Analog Twin Framework},
author = {Nazish Tahir and Ramviyas Parasuraman},
url = {https://sites.google.com/ualberta.ca/2022workshop-ai-for-hri-cr-ar},
year = {2022},
date = {2022-05-23},
booktitle = {ICRA 2022 Workshop on Intelligent Control Methods and Machine Learning Algorithms for Human-Robot Interaction and Assistive Robotics},
abstract = {Resource-constrained mobile robots that lack the capability to be completely autonomous can rely on a human or AI supervisor acting at a remote site (e.g., control station or cloud) for their control. Such a supervised autonomy or collaborative control of a robot poses high networking and computing capabilities requirements at both sites, which are not easy to achieve. This paper introduces and analyzes a new analog twin framework by synchronizing mobility between two mobile robots, where one robot acts as an analog twin to the other robot. We devise a novel collaborative priority-based bilateral teleoperation strategy for supervised goal navigation tasks to validate the proposed framework. The practical implementation of a supervised control strategy on this framework entails a mobile robot system divided into a master-client scheme over a communication channel where the Client robot resides on the site of operation guided by the Master robot through an agent (human or AI) from a remote location. The master robot controls the client robot with its autonomous navigation algorithm, which reacts to the predictive force received from the Client robot. We analyze the proposed strategy in terms
of network performance (throughput), task performance (goal reach accuracy), task efficiency, and computing efficiency (CPU utilization). Real-world experiments demonstrate the method’s novelty and versatility in realizing more practical reactive and collaborative planning and control applications.},
keywords = {control, multi-robot systems, networking},
pubstate = {published},
tppubtype = {workshop}
}

@article{Parasuraman2018b,
title = {Multi-Point Rendezvous in Multi-Robot Systems},
author = {Ramviyas Parasuraman and Jonghoek Kim and Shaocheng Luo and Byung-Cheol Min},
url = {https://ieeexplore.ieee.org/document/8472798},
doi = {10.1109/TCYB.2018.2868870},
isbn = {2168-2275},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Cybernetics},
volume = {50},
number = {1},
pages = {310-323},
abstract = {Multi-robot rendezvous control and coordination strategies have garnered significant interest in recent years because of their potential applications in decentralized tasks. In this paper, we introduce a coordinate-free rendezvous control strategy to enable multiple robots to gather at different locations (dynamic leader robots) by tracking their hierarchy in a connected interaction graph. A key novelty in this strategy is the gathering of robots in different groups rather than at a single consensus point, motivated by autonomous multi-point recharging and flocking control problems. We show that the proposed rendezvous strategy guarantees convergence and maintains connectivity while accounting for practical considerations such as robots with limited speeds and an obstacle-rich environment. The algorithm is distributed and handles minor faults such as a broken immobile robot and a sudden link failure. In addition, we propose an approach that determines the locations of rendezvous points based on the connected interaction topology and indirectly optimizes the total energy consumption for rendezvous in all robots. Through extensive experiments with the Robotarium multi-robot testbed, we verified and demonstrated the effectiveness of our approach and its properties.},
keywords = {control, herding, multi-robot systems, robotics},
pubstate = {published},
tppubtype = {article}
}

@conference{Yang2020b,
title = {Hierarchical Needs Based Self-Adaptive Framework For Cooperative Multi-Robot System},
author = {Qin Yang and Ramviyas Parasuraman},
url = {http://hero.uga.edu/wp-content/uploads/2020/10/SMC_2020___Qin_Yang___Hierarchical_Needs_Based_Multi_Robot_Task_Planning.pdf},
year = {2020},
date = {2020-10-11},
booktitle = {IEEE SMC 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS},
abstract = {Research in multi-robot and swarm systems has seen significant interest in cooperation of agents in complex and dynamic environments. To effectively adapt to unknown environments and maximize the utility of the group, robots need to cooperate, share information, and make a suitable plan according to the specific scenario. Inspired by Maslow’s hierarchy of human needs and systems theory, we introduce Robot’s Need Hierarchy and propose a new solution called Self-Adaptive Swarm System (SASS). It combines multi-robot perception, communication, planning, and execution with the cooperative management of conflicts through a distributed Negotiation-Agreement Mechanism that prioritizes robot’s needs. We also decompose the complex tasks into simple executable behaviors through several Atomic Operations, such as selection, formation, and routing. We evaluate SASS through simulating static and dynamic tasks and comparing them with the state-of-the-art collision-aware task assignment method integrated into our framework.},
keywords = {behavior-trees, multi-robot systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2019,
title = {Self-Reactive Planning of Multi-Robots with Dynamic Task Assignments},
author = {Qin Yang and Zhiwei Luo and Wenzhan Song and Ramviyas Parasuraman},
year = {2019},
date = {2019-08-22},
booktitle = {Int. Symp. on Multi Robot Systems (MRS)},
organization = {Rutgers, NJ, USA},
abstract = {Multi-Robot Systems and Swarms are intelligent systems in which a large number of agents are coordinated in a distributed and decentralized way. Each robot may have homogeneous or heterogeneous capabilities and can be programmed with several fundamental control laws adapting to the environment. Through different kinds of relationships built using the communication protocols, they present various behaviors based on the shared information and current state. To adapt to dynamic environments effectively, and maximize the utility of the group, robots need to cooperate, share their local information, and make a suitable plan according to the specific scenario. In this paper, we formalize the problem of multi-robots fulfilling dynamic tasks using state transitions represented through a Behavior Tree. We design a framework with corresponding distributed algorithms for communications between robots and negotiation and agreement protocols through a novel priority mechanism. Finally, we evaluate our framework through simulation experiments.},
keywords = {cooperation, multi-robot systems, planning},
pubstate = {published},
tppubtype = {conference}
}