The Application of Adaptive Ant-colony A* Hybrid Algorithm Based on Objective Evaluation Factor in RoboCup Rescue Simulation Dynamic Path Planning

Abstract

This paper proposed an adaptive A* Hybrid algorithm combining the advantages of ant colony and A* algorithm. Improvements of the traditional ant-colony algorithm was suggested, and the Target Evaluation Factor based on global dynamic information was introduced to promote the performance of ant path decision. In additional, an adaptive pheromone updating strategy was designed to balance and speed up the convergence rate. Test results showed that this algorithm can effectively guide agents to get an optimal path to ensure the efficient completion of tasks.

Time-Optimal Path Planning of a Hybrid Autonomous Underwater Vehicle Based on Ocean Current Neural Point Grid

“…Non-bio-inspired algorithms include the graph search probability method [7], the simulated annealing algorithm [8], the artificial potential field method [9,10], the A* algorithm [11], the Dijkstra algorithm [12], and the Floyd algorithm [13]. Bio-inspired algorithms include neural network algorithms [14], particle swarm algorithms [15,16], ant colony algorithms [17,18], and genetic algorithms [19]. A new path planning method has been proposed to plan the motion trajectory of a planar articulated robot in a static workspace [7].…”

“…In addition, a new intelligent path planning method based on artificial neural networks is also presented to avoid obstacles and improve the planning speed of mobile robots [14]. In order to combine the advantages of the ant colony algorithm and the A* algorithm, an adaptive A* hybrid algorithm is designed to obtain the optimal path [18]. Likewise, the improved D* algorithm based on particle swarm optimization is also employed to search for global routes in a dynamic workspace [15].…”