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1. Decentralized Data Collection via Swarm Contracts: Study on Crowd-Sourced Google Maps

   https://doi.org/10.1109/ICCAR57134.2023.10151727  

   Mallikarachchi, Sanjaya; Ho, Bonnie; Kanj, Iyad; Seneviratne, Oshani; Godage, Isuru S. (April 2023, 2023 9th International Conference on Control, Automation and Robotics (ICCAR))

   “Data is the new oil” has become a popular catch-phrase in the world of technology, emphasizing the immense value of data in today's digital age. Most services and platforms rely on data, but collecting this data can be challenging and costly. To address this issue, we leverage a novel distributed crowdsourcing framework - termed Swarm Contracts - that utilizes blockchain and is applied to robotics technologies. The framework encourages an incentivized crowdsourcing model through open-source robots and a secure, decentralized, and transparent blockchain-based incentive system. As a demonstration of the framework's capabilities, we use it to collect Google Street View ® map data, which can be a resource-intensive task to keep up to date using traditional centralized methods. Our Swarm Contract framework uses Google Street View ® Publish API, which allows for the contribution of street view data to Google Maps @to implement the incentive-based crowdsourcing of street view images. By incorporating a swarm contract-powered framework with the Google Street View ® Publish API, we show that the incentivized crowdsourcing of street view data can be a practical solution to maintain accurate and up-to-date Google Street View ® maps. 

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2. Managing Collaborative Tasks within Heterogeneous Robotic Swarms using Swarm Contracts

   https://doi.org/10.1109/DAPPS55202.2022.00014  

   Mallikarachchi, Sanjaya; Dai, Can; Seneviratne, Oshani; Godage, Isuru (August 2022, IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS))

   The growing number of applications in Cyber-Physical Systems (CPS) involving different types of robots while maintaining interoperability and trust is an ongoing challenge faced by traditional centralized systems. This paper presents what is, to the best of our knowledge, the first integration of the Robotic Operating System (ROS) with the Ethereum blockchain using physical robots. We implement a specialized smart contract framework called “Swarm Contracts” that rely on blockchain technology in real-world applications for robotic agents with human interaction to perform collaborative tasks while ensuring trust by motivating the agents with incentives using a token economy with a self-governing structure. The use of open-source technologies, including robot hardware platforms such as TurtleBot3, Universal Robot arm, and ROS, enables the ability to connect a wide range of robot types to the framework we propose. Going beyond simulations, we demonstrate the robustness of the proposed system in real-world conditions with actual hardware robots. 

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3. Reinforcement learning-based dynamic field exploration and reconstruction using multi-robot systems for environmental monitoring

   https://doi.org/10.3389/frobt.2025.1492526  

   Lu, Thinh; Sobti, Divyam; Talwar, Deepak; Wu, Wencen (March 2025, Frontiers in Robotics and AI)

   In the realm of real-time environmental monitoring and hazard detection, multi-robot systems present a promising solution for exploring and mapping dynamic fields, particularly in scenarios where human intervention poses safety risks. This research introduces a strategy for path planning and control of a group of mobile sensing robots to efficiently explore and reconstruct a dynamic field consisting of multiple non-overlapping diffusion sources. Our approach integrates a reinforcement learning-based path planning algorithm to guide the multi-robot formation in identifying diffusion sources, with a clustering-based method for destination selection once a new source is detected, to enhance coverage and accelerate exploration in unknown environments. Simulation results and real-world laboratory experiments demonstrate the effectiveness of our approach in exploring and reconstructing dynamic fields. This study advances the field of multi-robot systems in environmental monitoring and has practical implications for rescue missions and field explorations. 

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4. An Uncertainty-driven Sampling-based Online Coverage Path Planner for Seabed Mapping using Marine Robots

   https://doi.org/10.1109/AUV53081.2022.9965886  

   Zhou, Mingxi; Shi, Jianguang (September 2022, 2022 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV))

   Seabed mapping is a common application for marine robots, and it is often framed as a coverage path planning problem in robotics. During a robot-based survey, the coverage of perceptual sensors (e.g., cameras, LIDARS and sonars) changes, especially in underwater environments. Therefore, online path planning is needed to accommodate the sensing changes in order to achieve the desired coverage ratio. In this paper, we present a sensing confidence model and a uncertainty-driven sampling-based online coverage path planner (SO-CPP) to assist in-situ robot planning for seabed mapping and other survey-type applications. Different from conventional lawnmower pattern, the SO-CPP will pick random points based on a probability map that is updated based on in-situ sonar measurements using a sensing confidence model. The SO-CPP then constructs a graph by connecting adjacent nodes with edge costs determined using a multi-variable cost function. Finally, the SO-CPP will select the best route and generate the desired waypoint list using a multi-variable objective function. The SO-CPP has been evaluated in a simulation environment with an actual bathymetric map, a 6-DOF AUV dynamic model and a ray-tracing sonar model. We have performed Monte Carlo simulations with a variety of environmental settings to validate that the SO-CPP is applicable to a convex workspace, a non-convex workspace, and unknown occupied workspace. So-CPP is found outperform regular lawnmower pattern survey by reducing the resulting traveling distance by upto 20%. Besides that, we observed that the prior knowledge about the obstacles in the environment has minor effects on the overall traveling distance. In the paper, limitation and real-world implementation are also discussed along with our plan in the future. 

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5. Multi-Agent Trajectory Optimization Against Plan-Deviation Attacks using Co-Observations and Reachability Constraints

   https://doi.org/10.1109/CDC45484.2021.9683465  

   Yang, Ziqi; Tron, Roberto (December 2021, IEEE International Conference on Decision and Control)

   In this paper, we focus on using path planning and inter-agent measurements to improve the security of multi-robot systems against possible takeovers from cyber-attackers. We build upon recent trajectory optimization approaches where introspective measurement capabilities of the robots are used in an co-observation schedule to detect deviations from the preordained routes. This paper proposes additional constraints that can be incorporated in the previous trajectory optimization algorithm based on Alternating Direction Method of Multipliers (ADMM). The new constraints provide guarantees that a compromised robot cannot reach a designed safety zone between observations despite adversarial movement by the attacker. We provide a simulation showcasing the new components of the formulation in a multi-agent map exploration task with several safety zones. 

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