To evade the big and destructive obstacles in the real world scenario, such as bomb blast, nuclear activities, and fire breakdowns, robots are necessary. Robot path planning (RPP) problem is one of the interesting NP-hard problems in the world of robotics. The RPP problem can be dealt with, using swarm intelligence (SI) based optimisation algorithms. Teaching learning based optimisation (TLBO) algorithm is a very efficient and reliable swarm intelligence based algorithm in the history of optimisation. This paper proposed a hybridised version of TLBO with shuffled frog leaping algorithm (SFLA) to improve the efficiency in terms of exploitation and to overcome the slow convergence rate. The proposed variant is named as shuffled teaching learning-based optimisation (STLBO) algorithm. For checking the efficiency and accuracy of the proposed STLBO, it is applied to 12 continuous benchmark functions and compared with different nature inspired algorithms (NIA). To check the robustness of the propounded STLBO, it is implemented to solve the problem of RPP. Through simulation results and statistical analyses, the effectiveness of the proposed STLBO is proved.