A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 9; no. 11; p. 1243
Main Authors Ntakolia, Charis, Lyridis, Dimitrios V.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2021
Subjects
Algorithms
Automation
Computation
Computer applications
Computer vision
Decision making
Energy consumption
Fuzzy logic
Fuzzy systems
Intelligence
Machine vision
Marine transportation
maritime transportation
metaheuristic algorithms
Multiple objective analysis
Objectives
optimization
Optimization algorithms
Path planning
Performance evaluation
Quantum computing
Shipping industry
Shortest-path problems
Surface vehicles
Swarm intelligence
Traveling salesman problem
unmanned surface vehicles
Unmanned vehicles
Vehicles
Velocity
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Summary:Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse9111243