This research thesis highlights the techniques in the light of literature for path planning of robots.As the robotic missions into different spaces are increasing,the requirement for path planning has become stringent to enhance the success of these missions.missions.Considering the growing research base in the field of path planning of autonomous autonomous vehicles and mobile robots,it is important to investigate the evidence in this regard.The path planning strategies and algorithms broadly fall into four categories: reactive reactive computing,C-space search strategies,optimal control,and soft computing.These categories have their sub-methods for achieving the desired path planning control and optimization strategies.These methods work either in a conventional manner to map the path according to the given constraints or heuristic fashion to solve the path planning problem through machine learning approaches.The main challenge in designing paths for different robotic missions is the minimization of the computational cost and achieving stability in uncertain environmental conditions.The algorithms proposed thus far are used to to detect static impediments.The problem of organizing robot paths from source to target entails differentiating static and dynamic snags in the robot’s detecting region and deciding on a crash-free path.Most algorithms are focused on the trail-finding procedure in a very illustrious environment and leave higher-level functions like obstacle detection to chance,but the cognitive-based adaptive path planning is a reconciling and psychological feature-based mostly thinking system that identifies dynamic obstacles in an unknown environment.In general,the mobile robot is capable of detecting its surroundings,interpreting the detected information to determine its location and the environment,and calculating a real-time course to reach the item.The problem of obstacle avoidance is a critical topic to be addressed in this procedure.In this paper,an adaptive path-planning control method for the obstacle avoidance of a mobile robot is devised without extensive environmental knowledge,high memory capacity,and substantial compute burden.The robot may progressively approach its object using the motion tracking mode,obstacle avoidance mode,self-rotation mode,and robot state selection in this system.Numerical simulations of a differential-driving mobile robot under the possibility of obstacle forms are are used to validate the efficiency of the proposed adaptive path-planning control strategy.This paper offers a strategy for locating the best path in a dynamic environment.During this technique,the sensor-equipped mechanism processes the data received from the sensors and determines the path supported by the data processed.