This paper presents the implementation of smart cuckoo search (SCS) algorithm for intelligent path planning of mobile robots. A new fitness function is modeled and optimized by SCS algorithm to generate collision free optimal route for the mobile robots. The simulation results are illustrated to verify the ability of robot to deal with different environment conditions and reach to the target in all the time. Also the results obtained using SCS algorithm is compared with results of Adaptive Particle Swarm Optimization (APSO). It is noticed that SCS algorithm showed better results as compared to APSO. Finally the simulation platform results are validated with Khepera-IV mobile robot experimental results and it is revealed that proposed algorithm is valid and feasible in the mobile robot path planning problems.

本文介绍了用于移动机器人智能路径规划的智能布谷鸟搜索（SCS）算法的实现。通过SCS算法对新的适应度函数进行建模和优化，以为移动机器人生成无碰撞的最佳路线。仿真结果说明了该方法的有效性，以验证机器人应对各种环境条件并始终达到目标的能力。还将使用SCS算法获得的结果与自适应粒子群优化（APSO）的结果进行比较。值得注意的是，与APSO相比，SCS算法显示出更好的结果。最后用Khepera-IV移动机器人的实验结果对仿真平台的结果进行了验证，结果表明该算法在移动机器人路径规划问题上是有效可行的。