Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows
- URL: http://arxiv.org/abs/2609.12502v1
- Date: Fri, 11 Sep 2026 07:05:42 GMT
- Title: Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows
- Abstract summary: This paper introduces MACE, a decentralized exploration framework that actively evaluates whether establishing communication is worthwhile.<n>We demonstrate that MACE reduces the total exploration time by up to 23% compared to existing communication-constrained exploration strategies.
- Score: 16.619647268424412
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Exploring unknown environments with multi-robot teams can improve efficiency by allowing robots to explore in parallel. However, realizing these gains requires effective information sharing. When communication is intermittent, robots must balance the benefits of sharing information against the cost of diverting from exploration to establish communication. This paper introduces MACE, a decentralized exploration framework that actively evaluates whether establishing communication is worthwhile. At scheduled communication windows, robots estimate the cost of reaching previously identified communication locations. By formulating this decision as a variant of the Vehicle Orienteering Problem, robots evaluate routes based on the travel required to establish communication and the exploration that can be completed along the way. This approach enables robots to communicate more frequently than under purely opportunistic strategies while reducing the unnecessary travel associated with fixed rendezvous strategies. Across a set of simulated environments with varying size and geometry, we demonstrate that MACE reduces the total exploration time by up to 23% compared to existing communication-constrained exploration strategies.
Related papers
- Robots Ask the Way: Communication-Enabled Social Navigation [12.914876079465431]
We introduce Communication-enabled Social Navigation (CommNav)<n>In this novel task, robotic agents actively seek assistance from residents to locate target individuals.<n>We show that explicit human-robot communication substantially enhances multi-person navigation performance.
arXiv Detail & Related papers (2026-07-01T15:07:40Z) - SLEI3D: Simultaneous Exploration and Inspection via Heterogeneous Fleets under Limited Communication [10.496161804810473]
This work proposes a novel planning and coordination framework (SLEI3D) that integrates the online strategies for collaborative 3D exploration, adaptive inspection and timely communication.<n>The proposed framework is validated extensively via high-fidelity simulations of numerous large-scale missions with up to 48 robots and 384 thousand cubic meters.
arXiv Detail & Related papers (2026-01-01T02:06:35Z) - Air-Ground Collaboration for Language-Specified Missions in Unknown Environments [62.56917065429864]
We present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language.<n>We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot.
arXiv Detail & Related papers (2025-05-14T03:33:46Z) - LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [72.81786007015471]
We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.<n>CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.<n> Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
arXiv Detail & Related papers (2024-01-10T00:08:00Z) - From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban
Search and Rescue [46.377510400989536]
We present a novel hybrid algorithm for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information.
We redefine the local best and global best positions to suit scenarios without continuous target information.
The presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.
arXiv Detail & Related papers (2023-11-28T17:05:25Z) - Navigating to Success in Multi-Modal Human-Robot Collaboration: Analysis
and Corpus Release [2.467008547326945]
Human-guided robotic exploration is a useful approach to gathering information at remote locations.
Maintaining common ground between the remotely-located partners is a challenge.
We explore how participants utilized multiple modalities to investigate a remote location with the help of a robotic partner.
arXiv Detail & Related papers (2023-10-26T16:56:01Z) - A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops [72.1902268440158]
Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments.<n>The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time.<n>Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control.<n>This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
arXiv Detail & Related papers (2023-09-18T21:20:50Z) - Communication-Efficient Reinforcement Learning in Swarm Robotic Networks
for Maze Exploration [2.958532752589616]
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.
arXiv Detail & Related papers (2023-05-26T16:56:00Z) - Over-communicate no more: Situated RL agents learn concise communication
protocols [78.28898217947467]
It is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other.
Much research on communication emergence uses reinforcement learning (RL)
We explore situated communication in a multi-step task, where the acting agent has to forgo an environmental action to communicate.
We find that while all tested pressures can disincentivise over-communication, situated communication does it most effectively and, unlike the cost on effort, does not negatively impact emergence.
arXiv Detail & Related papers (2022-11-02T21:08:14Z) - Interpretation of Emergent Communication in Heterogeneous Collaborative
Embodied Agents [83.52684405389445]
We introduce the collaborative multi-object navigation task CoMON.
In this task, an oracle agent has detailed environment information in the form of a map.
It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals.
We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
arXiv Detail & Related papers (2021-10-12T06:56:11Z) - With Whom to Communicate: Learning Efficient Communication for
Multi-Robot Collision Avoidance [17.18628401523662]
This paper presents an efficient communication method that solves the problem of "when" and with "whom" to communicate in multi-robot collision avoidance scenarios.
In this approach, every robot learns to reason about other robots' states and considers the risk of future collisions before asking for the trajectory plans of other robots.
arXiv Detail & Related papers (2020-09-25T09:49:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.