Search papers, labs, and topics across Lattice.
This paper introduces a mixed integer linear programming (MILP) model for the joint optimization of static charging point placement and UAV path planning in urban environments, specifically for remote sensing image feature extraction tasks. The objective is to minimize total operational costs while adhering to UAV energy constraints and task demands. Simulation results using real-world urban datasets show a 9.1% reduction in energy consumption compared to heuristic approaches, demonstrating the model's efficacy.
Optimizing charging station placement and UAV paths together cuts energy use by 9% compared to doing it separately.
This paper addresses the joint optimization problem of static charging point layout and path planning for urban UAVs tasked with remote sensing image feature extraction. A mixed integer linear programming (MILP) model is proposed to minimize total operational costs while satisfying UAV energy constraints and task requirements. The model integrates charging infrastructure deployment, UAV trajectory design, and task allocation. Simulation results based on real-world urban datasets demonstrate the effectiveness of the proposed approach, achieving a 9.1% reduction in energy consumption compared to conventional heuristics.
