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Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach
E Balasubramanian1, E Elangovan2, P Tamilarasan3
1Department of Mechanical Engineering, Head-Centre for Autonomous System Research, Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology, Avadi, Chennai, Tamilnadu 600062 India.
Journal of Ambient Intelligence and Humanized Computing
|July 5, 2022
Summary
This study introduces a novel hybrid algorithm, MACO-MEA*, for Unmanned Aerial Vehicle (UAV) navigation. The algorithm significantly reduces energy consumption and execution time in complex 3D environments with obstacles.
Area of Science:
- Robotics and Control Systems
- Artificial Intelligence and Optimization Algorithms
- Aerospace Engineering and Navigation
Background:
- Autonomous Unmanned Aerial Vehicle (UAV) navigation demands optimal path planning for mission success.
- Existing methods face challenges in energy efficiency and computational complexity, especially in 3D, obstacle-rich environments.
- Need for robust algorithms to handle complex navigation constraints and minimize energy expenditure.
Purpose of the Study:
- To develop an energy-efficient optimal path planning algorithm for UAVs in 3D constrained and obstacle-prone regions.
- To integrate Modified Ant Colony Optimization (MACO) with a memory-efficient A* (MEA*) algorithm for enhanced pathfinding.
- To validate the proposed hybrid MACO-MEA* algorithm through simulations and real-time flight trials.
Main Methods:
- Developed a hybrid algorithm combining MACO for global path optimization and MEA* for efficient local obstacle avoidance.
- Enhanced MACO with improved pheromone strategies to mitigate local traps and premature convergence.
- Utilized MEA* to overcome memory limitations of traditional A* and improve grid traversal.
Main Results:
- The MACO-MEA* algorithm demonstrated a 21% reduction in energy consumption compared to MACO-A*.
- Achieved a 55% shorter execution time, indicating significant computational efficiency.
- Simulation and experimental results showed a 99% coherence in the traversed path, validating the algorithm's reliability.
Conclusions:
- The hybrid MACO-MEA* algorithm offers a viable and efficient solution for energy-aware UAV navigation in complex 3D environments.
- The integration of MACO and MEA* effectively addresses challenges in path planning, obstacle avoidance, and energy optimization.
- The algorithm's performance in simulations and real-world trials confirms its practical applicability for autonomous missions.
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