Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

移动机器人在各种应用中的可接受性导致移动机器人研究领域的增加。路径规划是需要在更高层次上临时完善的核心领域之一。如今，优化在其中扮演着更为重要的角色。自然启发算法在更大程度上促进了优化。本文介绍了使用锦标赛选择功能进行机器人路径规划的改良杜鹃搜索算法。路径长度和路径时间是算法的参数，用于验证输出的有效性和可接受性。布谷鸟搜索算法的基本工作原理作为基线，锦标赛选择功能适用于从初始位置导航到最终位置时计算机器人的最佳路径。比赛选择功能正在取代由布谷鸟搜索算法完成的随机选择的概念。比赛选择的使用克服了机器人在配置空间中遍历时的局部最小值，并增加了提供最佳结果的可能性。传统的布谷鸟搜索算法的随机选择机制可能导致过早收敛，可能会陷入局部最小值。锦标赛选择功能的使用增加了给出更好结果的可能性，因为它允许所有可能的解决方案参加锦标赛。分析结果并与其他相关工作（例如布谷鸟搜索算法和粒子群优化技术）进行比较，并在本文中进行介绍。所提出的方法在路径长度和路径时间方面产生了更好的输出。