دانلود رایگان مقاله انگلیسی روش برنامه ریزی مسیر با استفاده از الگوریتم کلونی مورچه برای صندلی های هوشمند - الزویر 2018

abstract

In many cases, users of smart wheelchairs have difficulties with daily maneuvering tasks and would benefit from an automated navigation system. With multi-colony division and cooperation mechanism, the polymorphic ant colony algorithm is helpful to solve optimal path planning problems by greatly improving search and convergence speed. In this paper, a path planning method for smart wheelchairs is proposed based on the adaptive polymorphic ant colony algorithm. To avoid ant colony from getting into local optimum in the process of reaching a solution,the adaptive state transition strategy and the adaptive information updating strategy were employed in the polymorphic ant colony algorithm to guarantee the relative importance of pheromone intensity and desirability. Subsequently, the search ant maintains the randomness for the search of the global optimal solution, and then the deadlock problem is solved by means of the direction determination method that improves the global search ability of the algorithm. The target path planning and obstacle path planning are respectively carried out by using the adaptive polymorphic ant colony algorithm. Experimental results indicate that the proposed method provides better performance than the improved ant colony algorithm and the polymorphic ant colony algorithm. Furthermore, the efficiency of finding an optimum solution is higher than the average polymorphic ant colony algorithm. The proposed method, which achieves superior performance in path planning for smart wheelchairs, is even racing ahead of other state-of-the-art solutions. In addition, this study reveals the feasibility of using it as an effective and feasible planning path tool for future healthcare systems.

5. Conclusions

In this paper, the adaptive polymorphic ant colony algorithm is proposed as a path planning method for smart wheelchairs. The search ant can determine the optimal combination parameters in accordance with actual situation and make the state transition in the search process to effectively prevent the search ant from falling into local optimum to a certain extent. The direction determining method also employed to accelerate convergence, improving the efficiency of the algorithm in searching the global optimal solution. The improved polymorphic ant colony algorithm is applied separately to the target path planning and obstacle path planning, and is compared with the improved ant colony algorithm and the generally polymorphic ant colony algorithm, respectively. Our method achieves superior performance in this challenging problem, which is evenracing aheadofthe recentlydevelopedstate-of-the-art solutions. Additionally, this study reveals the feasibility of using it as an effective and feasible planning path tool for future healthcare systems.

It is noteworthy that in a point to point path planning, the smart wheelchair is just regarded as an idealized point rather than a practical model in our experiment. There are lots of existing models regarding wheelchair or similar. Actually, safe mission planning typically seeks to construct a route from origin to destination that minimizes the risk imposed; nevertheless, the robot has limitation in making rotation during automatic drive. In the future study, we will consider some practical models for the smart wheelchair in path planning.