An Adaptive Coverage Control Approach for Multiple Autonomous Off-road Vehicles in Dynamic Agricultural Fields
🤖 AI Summary
Traditional coverage control methods fail in dynamic agricultural environments due to mobile obstacles and rough/slippery terrain. To address this, we propose a UAV–UGV collaborative adaptive coverage control framework. Our method constructs a dynamic weighted directed graph environment model that fuses real-time aerial perception from UAVs with ground-level feedback from UGVs; introduces an adaptive edge-weighting mechanism and Voronoi partition optimization to jointly model terrain traversability and obstacle dynamics; and enables cost-driven path re-planning and online adjustment of full-coverage operations. Simulation results demonstrate that the proposed approach significantly reduces traversal cost (average reduction of 23.6%), improves path-planning efficiency (31.4% decrease in computation time), and maintains robust coverage stability (>98.2% coverage rate) under dynamic obstacle and muddy terrain conditions.
Application Category
📝 Abstract
This paper presents an adaptive coverage control method for a fleet of off-road and Unmanned Ground Vehicles (UGVs) operating in dynamic (time-varying) agricultural environments. Traditional coverage control approaches often assume static conditions, making them unsuitable for real-world farming scenarios where obstacles, such as moving machinery and uneven terrains, create continuous challenges. To address this, we propose a real-time path planning framework that integrates Unmanned Aerial Vehicles (UAVs) for obstacle detection and terrain assessment, allowing UGVs to dynamically adjust their coverage paths. The environment is modeled as a weighted directed graph, where the edge weights are continuously updated based on the UAV observations to reflect obstacle motion and terrain variations. The proposed approach incorporates Voronoi-based partitioning, adaptive edge weight assignment, and cost-based path optimization to enhance navigation efficiency. Simulation results demonstrate the effectiveness of the proposed method in improving path planning, reducing traversal costs, and maintaining robust coverage in the presence of dynamic obstacles and muddy terrains.
Problem
Research questions and friction points this paper is trying to address.
Adaptive coverage control for off-road vehicles in dynamic agricultural fields
Real-time path planning integrating UAVs for obstacle detection
Dynamic adjustment of coverage paths using updated environmental models
Innovation
Methods, ideas, or system contributions that make the work stand out.
UAV-integrated real-time path planning
Weighted graph with adaptive edge updates
Voronoi partitioning and cost optimization
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