Grid-Based coverage path planning with NFZ avoidance for UAV using parallel self-adaptive ant colony optimization algorithm in cloud IoT

• Published in: Journal of cloud computing : advances, systems and applications Vol. 11; no. 1; pp. 1 - 28
• Main Authors: Gong, Yiguang, Chen, Kai, Niu, Tianyu, Liu, Yunping
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022; Springer Nature B.V; SpringerOpen
• Subjects: AI-empowered Resource Provision and Service Scheduling in Multi-Clouds; Algorithms; Ant colony algorithm; Ant colony optimization; Cloud computing; Completion time; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Coverage path planning; Energy consumption; Floyd algorithm; Grid-based technique; Information Systems Applications (incl.Internet); Internet of Things; New technology; No-fly zone; Optimization algorithms; Parameter modification; Path planning; Performance enhancement; Power; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Unmanned aerial vehicle; Unmanned aerial vehicles; Floyd algorithm; No-fly zone; Grid-based technique; Unmanned aerial vehicle; Coverage path planning; Ant colony algorithm
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-022-00298-2

In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.