RobotFleet: An Open-Source Framework for Centralized Multi-Robot Task Planning
Coordinating heterogeneous robot fleets to achieve multiple goals is challenging in multi-robot systems. We introduce an open-source and extensible framework for centralized multi-robot task planning and scheduling that leverages LLMs to enable fleets of heterogeneous robots to accomplish multiple tasks. RobotFleet provides abstractions for planning, scheduling, and execution across robots deployed as containerized services to simplify fleet scaling and management. The framework maintains a shared declarative world state and two-way communication for task execution and replanning. By modularizing each layer of the autonomy stack and using LLMs for open-world reasoning, RobotFleet lowers the barrier to building scalable multi-robot systems. The code can be found here: https://github.com/therohangupta/robot-fleet.
Code (0)
등록된 구현이 없습니다.
Tasks
Robot Task PlanningSimilar Papers 제목 키워드 기반
DVM-SLAM: Decentralized Visual Monocular Simultaneous Localization and Mapping for Multi-Agent Systems
Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in mapping unknown environments while simultaneously estimating their own positions. This approach enhances robustness, …
Autonomous NavigationCollision AvoidanceSimultaneous Localization and MappingDeAR: Decentralized Agentic Reasoning via Capability Grounding and Collaborative Thought Navigation
Existing agentic reasoning systems typically rely on centralized protocols. This design introduces routing bottlenecks and static role allocations that often fail when handling complex multimodal queries. We propose DeAR…
Multimodal ReasoningNeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack of a unified execution stack. This paper presents NeuroMesh, a multi…
Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled target domain within a decentralized frame…
Dictionary LearningDomain AdaptationSpillover Algorithm: A Decentralized Coordination Approach for Multi-Robot Production Planning in Open Shared Factories
Open and shared manufacturing factories typically dispose of a limited number of robots that should be properly allocated to tasks in time and space for an effective and efficient system performance. In particular, we de…