An official website of the United States government
We present a dynamic multi-robot mapping framework that combines Blockchain technology for swarm management with a Hybrid Ant Colony Optimization (HACO) algorithm for path planning. Blockchain-based swarm contracts enable decentralized, transparent, and secure task allocation, acceptance, tracking, and reward distribution among multiple robots. HACO facilitates efficient path planning in complex environments through cooperative and competitive strategies. We deploy multiple LiDAR-equipped Unitree Go2 dog robots to collaboratively and competitively map divided sub-areas, with task reassignment based on real-time feedback and the selected strategy. In cooperative mode, robots share data to boost efficiency and accuracy; in competitive mode, they work independently to reduce redundancy and optimize resources. Swarm contracts also verify full sub-area coverage via the merged map. Results show that integrating blockchain-based management with HACO significantly enhances mapping performance, delivering a robust and scalable solution for realworld multi-robot systems.
more »
« less
This content will become publicly available on March 25, 2026
Reinforcement learning-based dynamic field exploration and reconstruction using multi-robot systems for environmental monitoring
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.
more »
« less
- Award ID(s):
- 2148353
- PAR ID:
- 10592587
- Publisher / Repository:
- Frontiers in Robotics and AI
- Date Published:
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 12
- ISSN:
- 2296-9144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distributions while using only single-robot data. Our algorithm, Multi-robot Multi-model planning Diffusion (MMD), does so by combining learned diffusion models with classical search-based techniques – generating data-driven motions under collision constraints. Scaling further, we show how to compose multiple diffusion models to plan in large environments where a single diffusion model fails to generalize well. We demonstrate the effectiveness of our approach in planning for dozens of robots in a variety of simulated scenarios motivated by logistics environments. View video demonstrations in our supplementary material, and our code at: github.com/yoraish/mmd.more » « less
-
Integer programming (IP) has proven to be highly effective in solving many path-based optimization problems in robotics. However, the applications of IP are generally done in an ad-hoc, problem-specific manner. In this work, after examined a wide range of path-based optimization problems, we describe an IP solution methodology for these problems that is both easy to apply (in two simple steps) and high-performance in terms of the computation time and the achieved optimal- ity. We demonstrate the generality of our approach through the application to three challenging path-based optimization problems: multi-robot path planning (MPP), minimum constraint removal (MCR), and reward collection problems (RCPs). Associ- ated experiments show that the approach can efficiently produce (near-)optimal solutions for problems with large state spaces, complex constraints, and complicated objective functions. In conjunction with the proposition of the IP methodology, we introduce two new and practical robotics problems: multi-robot minimum constraint removal (MMCR) and multi-robot path planning (MPP) with partial solutions, which can be quickly and effectively solved using our proposed IP solution pipeline.more » « less
-
null (Ed.)Abstract In human–robot collaborative tasks, the performance of robot path planning has a direct impact on the robot-to-human hand-over process, or even the collaboration quality. In this work, we propose an evaluation study on multiple robot path planners with different metrics and reveal their pros and cons in representative human–robot collaborative manufacturing contexts. Afterward, based on the proposed metrics, we define a cost function for the dual-arm robot to choose optimized path planning solutions with maximum efficiency for its human partner in human–robot collaboration. We implement the proposed evaluation and optimization approaches to multiple realistic human–robot collaborative manufacturing contexts. Experimental results and evaluations suggest that our approaches are able to provide positive solutions for the robot path planner selection and also open a window for exploring more complicated and general robot path planning applications to human–robot collaborative tasks in smart manufacturing contexts.more » « less
-
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.more » « less
