ABSTRACT The use of unmanned aerial vehicles (UAVs) has been considered to be an efficient platform for monitoring critical infrastructures spanning over geographical areas. UAVs have also demonstrated exceptional feasibility when collecting data due to the wide wireless sensor networks in which they operate. Based on environmental information such as prohibited airspace, geo-locational conditions, flight risk, and sensor deployment statistics, we developed an optimal flight path planning mechanism by using multiobjective bio-inspired algorithms. In this paper, we first acquire data sensing points from the entire sensor field, in which UAV communicates with sensors to obtain sensor data, then we determine the best flight path between neighboring acquisition points. Using the proposed joint genetic algorithm and ant colony optimization from possible UAV flight paths, an optimal one is selected in accordance with sensing, energy, time, and risk utilities. The simulation results show that our method can obtain dynamic environmental adaptivity and high utility in various practical situations. INDEX TERMS Bio-inspired algorithms, multi-objectives, optimal path, sensor networks, unmanned aerial vehicle.
Yang and Yoo REF presented an optimal flight path planning mechanism using a multi-objective bio-inspired algorithm based on environmental information such as forbidden areas, geographical location conditions, flight risk, and sensor deployment statistics.
Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective Bio-Inspired Algorithms
IEEE Access
IEEE Access
Computer Science