Enhanced Hybrid Artificial Fish Swarm Algorithm for Three-Dimensional Path Planning Applied to Robotic Systems

Path planning is a vital challenge in robot navigation. In the real world, robots operate in 3D environments with various obstacles and restrictions. An improved artificial fish swarm algorithm (AFSA) is proposed to solve 3D path planning problems in environments with obstacles. The improved AFSA incorporates a 3D model of 24 possible movement points to more realistically simulate real-world robot movement capabilities. Several improvements are adopted, such as methods of simple and advanced 3D elimination. The 3D implementation of an agent’s navigation model, called an “obstacle heatmap”, is also presented. The use of a safety value factor and a total movement point factor in the AFSA’s objective function are introduced. The combination of improved AFSA and a ray-casting algorithm is also presented. Finally, a method called “multiple laser activation” is introduced to overcome both the main disadvantage of the application of AFSAs in path planning and the limitation of the finite number of possible movement points that appear when bio-inspired algorithms are applied to generate the optimal path in a grid environment. The resulting path is applied to real-world challenges with drones, coordinate measuring machines, and industrial robotic arms.