Energy-efficient green ant colony optimization for path planning in dynamic 3D environments

With the proliferation in demand for navigation systems for reconnaissance, surveillance, and other day-to-day activities, the development of efficient and robust path planning algorithm is an open challenge. The uncertain and dynamic nature of the real-time scenario imposes a challenge for the auto...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2021-03, Vol.25 (6), p.4749-4769
Main Authors: Sangeetha, V., Krishankumar, R., Ravichandran, K. S., Kar, Samarjit
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Artificial Intelligence
Autonomous navigation
Clean energy
Collision avoidance
Computational Intelligence
Control
Energy consumption
Energy efficiency
Engineering
Fault diagnosis
Genetic algorithms
Heuristic methods
Knowledge
Logistics
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Moving obstacles
Navigation systems
Obstacle avoidance
Octrees
Optimization
Path planning
Pheromones
Planning
Real time
Robotics
Robots
Simulation
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Summary:With the proliferation in demand for navigation systems for reconnaissance, surveillance, and other day-to-day activities, the development of efficient and robust path planning algorithm is an open challenge. The uncertain and dynamic nature of the real-time scenario imposes a challenge for the autonomous systems to navigate in the environment, avoiding collision with the moving obstacles without compromising on the energy-time trade-off. Motivated by this challenge, an efficient gain - based dynamic green ant colony optimization (GDGACO) metaheuristic has been proposed in this paper. The energy consumption while path planning in a dynamic scenario will be humongous owing to its nature. The proposed algorithm reduces the total energy consumed during path planning through an efficient gain function-based pheromone enhancement mechanism. The memory efficiency of Octrees is incorporated for workspace representation because of its ability to map large 3D environments to limited memory. Comprehensive simulation experiments are conducted to demonstrate the efficacy of GDGACO. Results are analysed through comparison with other methods in terms of path length, computation time, and energy consumed . Also, the results are verified for statistical significance.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-05483-6