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 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.