Optimal Path Planning of Mobile Robot using Hybrid Cuckoo Search-Bat Algorithm

Abstract

The mobile robot path planning depends on sensing the data, map building and planning the path according to the prescribed environment. Many researchers have followed different techniques to get the optimal path. In the Earlier days, mathematical model has been developed to get the optimal path but the result obtained was very poor. After that, so many Soft computing techniques have been developed, but the major drawback is that they consumes more time to find the optimal path. These algorithms sometimes fall under local optima during execution. In this thesis a mobile robot path planning is obtained by hybrid algorithm, developed by two nature inspired meta-heuristic algorithms namely Cuckoo-Search and Bat Algorithm in the unknown or partially known environment.
Cuckoo-Search is based on the parasitic behavior of the cuckoo, and the Bat Algorithm is based on the Echolocation behavior of the bats. The best qualities in the cuckoo-search and the bat algorithm are combined to form hybrid algorithm to obtain the optimal path. Proposed method is implemented on mobile robot in different environment to reach the target in the presence of static obstacles. The proposed algorithm perform good compared to the individual algorithms in terms of time of executions. Finally, experimental model has been prepared to test the obstacle avoidance criteria.