UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
🤖 AI Summary
Post-disaster infrastructure damage severely impairs the efficiency of medical resource delivery. To address this challenge, this paper proposes a tightly coupled unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) aerial-ground cooperative system for dynamic disaster scenes, enabling joint task allocation and collision-free trajectory planning for medical supply delivery. The method innovatively integrates genetic algorithms (GA) for multi-objective task assignment, informed-RRT* for asymptotically optimal path generation, and covariance matrix adaptation evolution strategy (CMA-ES) for spatiotemporal sequence scheduling optimization—constituting the first framework achieving tight heterogeneity-aware coordination under environmental uncertainty. Simulation results demonstrate a 23.6% reduction in total mission completion time and an 18.4% decrease in aggregate travel distance compared to baseline approaches. The system exhibits strong scalability and practical deployability for real-world emergency response scenarios.
Application Category
📝 Abstract
In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Random Tree Star) algorithm for collision-free trajectory generation. Further optimization of task sequencing and path efficiency is conducted using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Simulation experiments conducted in a realistic post-disaster environment demonstrate that our proposed approach significantly improves the overall efficiency of medical rescue operations compared to traditional strategies, showing substantial reductions in total mission completion time and traveled distance. Additionally, the cooperative utilization of UAVs and UGVs effectively balances their complementary advantages, highlighting the system' s scalability and practicality for real-world deployment.
Problem
Research questions and friction points this paper is trying to address.
Optimize UAV-UGV cooperation for medical rescue in disasters
Develop efficient task allocation using Genetic Algorithm
Generate collision-free paths with informed-RRT* and CMA-ES
Innovation
Methods, ideas, or system contributions that make the work stand out.
UAV-UGV cooperative framework for medical rescue
Genetic Algorithm optimizes task allocation
Informed-RRT* and CMA-ES enhance trajectory efficiency
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