Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation

2026-10-05

AI Summary

This study addresses the neglect of human physical constraints and the absence of energy consumption modeling in human-robot collaborative search and rescue by proposing a joint path planning method. For the first time, the Pandolf-Santee metabolic model and a rolling resistance energy consumption model are integrated into a multi-agent planning framework. Combined with procedural environment simulation, the approach dynamically optimizes handover points by leveraging terrain traversability differences between humans and unmanned ground vehicles, thereby achieving a complementary capability-based collaboration strategy that minimizes mission time. Experimental results demonstrate that, compared to baseline methods, the proposed approach reduces mission time by 5.3% and human energy expenditure by 17.8%, significantly outperforming conventional strategies.

Abstract

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.

Problem

• Human-UGV teaming
• Cooperative path planning
• Casualty evacuation
• Traversability
• Heterogeneous multi-agent

Innovation

• Cooperative Path Planning
• Human-UGV Teaming
• Traversability-Aware
• Metabolic Cost Model
• Switch Point Optimization