Graph-based robot optimal path planning with bio-inspired algorithms

• Published in: Biomimetic intelligence and robotics Vol. 3; no. 3; p. 100119
• Main Authors: Lei, Tingjun, Sellers, Timothy, Luo, Chaomin, Carruth, Daniel W., Bi, Zhuming
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2023; Elsevier
• Subjects: Autonomous robot; Bio-inspired algorithm; Graph-based model; Improved seagull optimization algorithm (iSOA); Path planning; Autonomous robot; Graph-based model; Path planning; Improved seagull optimization algorithm (iSOA); Bio-inspired algorithm
• ISSN: 2667-3797; 2667-3797
• DOI: 10.1016/j.birob.2023.100119

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas–Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.