Swarm animation is an important research direction in the field of computer animation, which aims at computer simulation of swarm behavior, real-time rendering of swarm animation and other relevant aspects. In these aspects, swarm path planning is a key issue and has received widespread attention. Some global path planning methods, such as A* and Dijkstra, due to need predefined environment information and high computation burden, is not suitable for swarm path planning; But some optimization algorithms based on swarm intelligence theory, such as particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC), etc. Because of its simplicity and fast convergence, has become the best choice of swarm path planning. This paper presents a multi-layer swarm path planning method. Firstly we divide the virtual environment into three layers, including geometry layer, topology layer and navigation layer. On the basis of this environmental model, the swarm path planning model is divided into inner layer and outer layer model, outer layer adopts an improved A* algorithm for topological path planning, inner layer adopts a hybrid algorithm based on ABC and PSO for dynamic path planning, then path data derived from two layers are joined together to form a final path result. Experimental results show this multi-layer swarm path planning method effectively reflected intelligence and authenticity of individuals, improved execution efficiency of the algorithm, and prevented premature convergence.
