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.
