UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments (2506.06136v1)

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.

• The paper introduces a UAV-UGV system that uses genetic algorithms, Informed-RRT*, and CMA-ES to optimize task allocation and trajectory planning.
• It demonstrates significant reductions in mission completion time and total path lengths compared to traditional methods.
• The study offers a practical approach for efficient resource delivery in post-disaster settings, paving the way for advanced cooperative robotic systems.

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments

In the paper titled "UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments," the authors present a sophisticated framework designed for enhancing the efficiency of medical resource delivery in post-disaster scenarios. Recognizing the limitations imposed by severely damaged infrastructure, this paper proposes a cooperative system leveraging both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The framework aims to optimize trajectory and task allocation through a hybrid approach utilizing genetic algorithms (GA) for task assignment and the Informed-RRT* algorithm for trajectory planning, further refined using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

Methodological Contributions

The framework addresses distinct challenges present in post-disaster environments by focusing on the following areas:

1. Task Allocation: An enhanced GA is used to allocate tasks efficiently among a heterogeneous team of UAVs and UGVs, ensuring improved operational synergy and execution efficiency. The GA dynamically adapts to the environment's constraints, potentially reducing mission completion time and energy consumption.
2. Trajectory Planning: The Informed-RRT* algorithm is applied for initial path generation, ensuring collision-free trajectories even within cluttered environments. This method restricts sampling to promising regions, enhancing convergence speed and path optimality.
3. Trajectory Optimization: CMA-ES further refines the sequence of task execution by minimizing travel distance and optimizing path efficiency. This optimization leads to smoother, more practical trajectories that adhere to operational constraints.

Simulation and Results

Simulations conducted in a realistic 20 km × 20 km post-disaster urban environment demonstrate the framework’s effectiveness. The results showcase significant improvements in total mission completion time and path lengths compared to traditional strategies, validating the practical deployment of UAV-UGV cooperative systems. Noteworthy findings include:

• Reduction in Completion Time: The proposed method significantly reduces mission time compared to random and K-Means clustering based task allocations.
• Path Length Efficiency: The use of CMA-ES results in substantial reductions in total path length, thereby optimizing energy consumption and operational costs.

Implications and Future Prospects

The implications of this research are multifaceted. Practically, the efficient deployment of UAV-UGV systems can drastically improve response times and resource utilization in disaster-affected areas, directly influencing survival rates and recovery outcomes. Theoretically, this paper lays the groundwork for future exploration into more complex cooperative robotic systems that can adapt to dynamic environments.

Future advancements may focus on integrating real-time environment mapping, enhancing inter-vehicle communication protocols, and extending the framework to accommodate evolving task priorities and real-time dynamic constraints. Additionally, incorporating machine learning techniques for predictive analytics and autonomous decision-making, the framework could evolve into a robust solution capable of tackling a wider range of disaster scenarios.

In conclusion, the paper presents a well-validated approach for UAV-UGV cooperation in disaster settings, offering substantive improvements over conventional methods. It paves the way for more resilient and adaptive robotic systems poised to enhance emergency response capabilities in critical post-disaster scenarios.