Dynamic UGV-UAV Cooperative Path Planning in Uncertain Environments

TL;DR

This paper proposes strategies for cooperative path planning between UGVs and UAVs in uncertain environments, optimizing safety and efficiency in scenarios like disaster response.

Contribution

It introduces a bidirectional approach and evaluates multi-UAV cooperation, demonstrating improved UGV travel times in uncertain urban road networks.

Findings

Bidirectional strategy outperforms other methods in most cases.

Multiple UAVs reduce UGV travel time.

Increased UAVs lead to higher computation time.

Abstract

This paper addresses the Dynamic UGV-UAV Cooperative Path Planning (DUCPP) problem involving one unmanned ground vehicle (UGV) assisted by one or more unmanned aerial vehicles (UAVs) operating on an uncertain road network with potentially impassable edges. DUCPP is particularly relevant for scenarios such as disaster response, emergency supply transport, and rescue operations, where a UGV must reach a specified destination in the presence of partially unknown road conditions. To enable the UGV to travel safely and efficiently to its destination, the UAV(s) dynamically inspect edges in the environment to identify and prune damaged or impassable edges from consideration. We present multiple strategies, including a bidirectional approach, to optimize UGV-UAV cooperation for finding a safe path in an uncertain road network. Furthermore, we explore the impact of using multiple UAVs on reducing the UGV's travel time, and evaluate the associated computation time. The proposed strategies are implemented and evaluated on 100 urban road networks. The results demonstrate that the bidirectional strategy achieves the best performance in most instances, and using multiple UAVs further reduces UGV travel time at the expense of increased computation time. This paper presents a robust framework for DUCPP to achieve efficient UGV-UAV cooperation for path planning and inspection, offering practical solutions for navigation in challenging and uncertain conditions.