Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access

Abstract

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Data Collection in Areas without Infrastructure Using LoRa Technology and a Quadrotor

“…Thus, the results shown serve as a basis for the development of future work focused on the generation and implementation of solution proposals focused on UAV-LoRa systems, for example, optimal trajectory planning for UAVs in data collection tasks with sensor nodes [30][31][32], energy minimization of UAV-LoRa systems [33,34], and protocols for efficient data transmission in networks [35]. Addressing the problems above requires the construction and instrumentation of a UAV-LoRa system and knowledge of the parameters that influence data collection (effects of LoS on the signal-to-noise ratio (SNR) and signal intensity), which is addressed in this work.…”

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

“…Park [33] presented an online distributed trajectory planning algorithm for a quadrotor cluster in a maze-like dynamic environment. Zhang [34] proposed a novel strategy that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. Chen [35] proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles and a biomimetic multi-USV cluster collaborative hunting method.…”