Metraipolis › archive › dc_85679ebac91c

Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation — arXiv:2610.06487

Source: arXiv
Original: https://arxiv.org/abs/2610.06487
License (as archived): CC0-1.0
Archived: 2026-10-06T05:30:14.050Z
SHA-256 of the archived text: 95732f6c76af5e34ad772b79d961ac52250eef2c9d2434c3003a3ee21c63aec1
Stable URL of this page: https://metraipolis.com/documento/dc_85679ebac91c

How to cite

Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation — arXiv:2610.06487 — arXiv, https://arxiv.org/abs/2610.06487 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_85679ebac91c (Metraipolis archive dc_85679ebac91c, sha256 95732f6c76af5e34ad772b79d961ac52250eef2c9d2434c3003a3ee21c63aec1)

BibTeX

@misc{dc85679ebac91c,
  title = {Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation — arXiv:2610.06487},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.06487},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_85679ebac91c; sha256 95732f6c76af5e34ad772b79d961ac52250eef2c9d2434c3003a3ee21c63aec1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.06487. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Heterogeneous multi-robot path planning is a well-studied problem in which agents with disparate kinematic and dynamic models must coordinate to achieve shared objectives. These formulations, however, treat all agents as robotic-their cost models are mechanical and their traversability is sensor-derived. In human-robot teaming, the human partner remains relegated to command and supervisory roles rather than being modeled as a physical co-navigator with distinct mobility constraints and dynamic energy reserves. This work investigates joint path planning for a two-agent human-UGV team in search-and-rescue casualty retrieval scenarios. We model the human agent using the Pandolf-Santee metabolic cost model with fatigue-modulated speed, and the UGV using a rolling-resistance energy model with terrain-dependent speed limits. By exploiting the complementary traversability of each agent-the human's ability to traverse dense vegetation and shallow water versus the UGV's superior speed on open terrain and roads-we optimize casualty transfer locations, termed switch points, to minimize total mission time. Evaluated across multiple synthetic 1km2 environments with procedurally generated elevation and land-cover data, the optimized strategy reduces mean mission time by 5.3% relative to a human-only baseline and by 7.0% relative to a naive human-UGV strategy without switch point optimization, while reducing human energy expenditure by 17.8% relative to baseline. Notably, the naive strategy reduces human energy expenditure by a larger margin (22.4%) but incurs a 2% increase in mission time relative to baseline, illustrating that switch point optimization is necessary to realize time savings from human-UGV teaming.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.HC, nuovi lavori del giorno).
Categorie arXiv: Robotics (cs.RO) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2610.06487 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T05:26:50.349Z dalla pagina https://arxiv.org/list/cs.HC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).