Safety-Aware UAVs Formation Scheme for Guiding UGVs through Obstacle-Laden Environments
Ruikang Xiao, Shuting Wang, Yuanlong Xie, Sheng Quan Xie, Youmin Zhang
AI summary
Problem
Existing methods fail to efficiently plan obstacle-avoidance strategies for entire UAV-UGV swarms and isolate formation control from path planning, causing degraded tracking accuracy and safety risks in unstructured environments.
Approach
The scheme generates a safe corridor using an improved A* algorithm with a novel line-of-sight mechanism, fits a minimum-snap trajectory to it, and employs a rigid-graph-based controller to dynamically scale the UAV formation for safe UGV guidance.
Key results
- Trajectory-bridged formation scheme unifying safe corridor planning and rigid-graph control
- Novel line-of-sight mechanism optimizing A* waypoints for whole-formation obstacle avoidance
- Rigid-graph-based controller enabling dynamic formation scaling within safe corridors
- Experimental validation showing enhanced robustness and superior planning over traditional methods
Why it matters
Provides a computationally efficient and safety-critical framework for heterogeneous UAV-UGV swarms operating in complex, unstructured environments.
Abstract
Using uncrewed aerial vehicle (UAV) formations to guide uncrewed ground vehicles (UGVs) through unstructured obstacle-laden areas leads to highly efficient execution of tasks such as the transportation of supplies. However, existing meth- ods fail to efficiently plan obstacle-avoidance strategies for the entire UAV-UGV swarm. Additionally, the formation controller and planner are isolated, resulting in the degradation of formation tracking accuracy, which presents potential security risks. This paper proposes a novel UAV formation scheme that integrates safe corridor (SC) generation, trajectory fitting, and formation tracking to ensure operational safety. The scheme employs a novel line-of- sight (LOS) mechanism to optimize A*-planned waypoints, gener- ating the SC as an obstacle-avoidance strategy. A minimum snap trajectory is fitted to the optimized waypoints with SC constraints. Bridged by the trajectory, the scheme develops a rigid-graph-based controller (RGC) to track the planning result, enabling dynamic formation maneuvering within the SC. Consequently, the proposed UAV formation scheme achieves obstacle-avoidance guidance by restricting the UGVs to the formation projection. The validation results demonstrate that the proposed scheme exhibits enhanced robustness and superior planning capabilities compared to tradi- tional methods.