Gaussian Adaptive Strategy Based Multi-Objective Evolutionary Optimization for Path Planning on Uneven Terrains

To enable mobile robots to safely and effectively accomplish path planning tasks on uneven terrains, this letter proposes a Gaussian adaptive strategy-based multi-objective evolutionary optimization. Firstly, path solutions are generated by employing spline interpolation on the digital elevation model of the terrain map. Subsequently, a multi-objective optimization function is constructed, considering parameters such as path length, uniformity, slope, and relief. These parameters are computed using the elevation values of the terrain. Secondly, an evolutionary optimization algorithm based on a Gaussian adaptive strategy is introduced. This strategy ensures the uniform distribution, adaptability, and size stability of the reference points set. Additionally, it controls the selection rate of non-dominant contribution points during the optimization process. Finally, experimental results conducted in five different real environments demonstrate the suitability of the proposed algorithm for solving path planning problems on uneven terrain.