arXiv (All)
open access
MA-LIPP: Cooperative Multi-Agent Load-Aware Informative Path Planning for Heterogeneous Robot Teams
Field robotics missions often require physical samples to be returned to laboratories for analysis, making path planning inherently load-aware and order-dependent as accumulated samples increase payload and traversal energy costs. In single-robot Load-Aware Informative Path Planning (LIPP), this rig
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Cooperative RSU Sleep Scheduling for Green V2I Corridors#321787
- Virtualized Resources Scheduling in Multi-Tenant and Multi-Data Centers Based on Artificial Intelligence Algorithm: A Review#490922
- SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning#1049508
- SAGE: Optimal-Stopping Peer Selection for Decentralised Federated Learning#1028661
- Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving#616902
Record · ID 1035135
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive