Skip to Main Content
Traditionally, path planning for robots is modeled as optimization problems. One of the most commonly used optimization strategies is Genetic Algorithm (GA) since it always guarantees a nearly shortest path even a global path can not be obtained within a reasonable time. In fact, the performance enhancement of GA is still an open topic for researchers by designing different genetic operators. In addition to this, we propose a new criterion for the selection of genetic operators during evolution in order to further facilitate the searching efficiency. In this paper, we propose a generalized 3-D path planning method for robots using GA with an adaptive evolution process. Based on the framework of traditional GA, we first introduce a new genetic operator, called Bind-NN which randomly divides and recombines an elitist chromosome based on nearest neighbor. We also show that by choosing the fitness variance of the shortest path in last generations as a guidance in selecting genetic operators during evolution, the search efficiency can be significantly improved, thus proposing a genetic operators selection scheme. In the latter part of this paper, we present the algorithm evaluation by stimulating a sample path planning problem on a structural frame. With the use of the proposed genetic operator and selection scheme, experimental results show a significant improvement in terms of search effectiveness and efficiency.