A Green Ant-Based method for Path Planning of Unmanned Ground Vehicles

Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint pow...

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Bibliographic Details
Published inIEEE access Vol. 5; pp. 1820 - 1832
Main Authors Jabbarpour, Mohammad Rzea, Zarrabi, Houman, Jung, Jason J., Pankoo Kim
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Ant colony optimization
Collision avoidance
Computing time
Energy consumption
evolutionary computation
Genetic algorithms
Intelligent vehicles
Iterative methods
Military computing
Navigation
Particle swarm optimization
Path planning
Performance evaluation
Power consumption
Power demand
Power management
Prediction algorithms
Prediction models
Robots
Shortest path planning
Travel time
Unmanned ground vehicles
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Summary:Planning of optimal/shortest path is required for proper operation of unmanned ground vehicles (UGVs). Although most of the existing approaches provide proper path planning strategy, they cannot guarantee reduction of consumed energy by UGVs, which is provided via onboard battery with constraint power. Hence, in this paper, a new ant-based path planning approach that considers UGV energy consumption in its planning strategy is proposed. This method is called Green Ant (G-Ant) and integrates an ant-based algorithm with a power/energy consumption prediction model to reach its main goal, which is providing a collision-free shortest path with low power consumption. G-Ant is evaluated and validated via simulation tools. Its performance is compared with ant colony optimization, genetic algorithm, and particle swarm optimization approaches. Various scenarios were simulated to evaluate G-Ant performance in terms of UGV travel time, travel length, computational time by taking into account different numbers of iterations, different numbers of obstacle, and different population sizes. The obtained results show that the G-Ant outperforms the existing methods in terms of travel length and number of iteration.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2656999