In Path planning Problems, a complete description of robot geometry, environments and obstacle are presented; the main goal is routing, moving from source to destination, without dealing with obstacles. Also, the existing route should be optimal. The definition of optimality in routing is the same as minimizing the route, in other words, the best possible route to reach the destination. In most of the routing methods, the environment is known, although, in reality, environments are unpredictable; But with the help of simple methods and simple changes in the overall program, one can see a good view of the route and obstacles ahead. In this research, a method for solving robot routing problem using cellular automata and genetic algorithm is presented. In this method, the working space model and the objective function calculation are defined by cellular automata, and the generation of initial responses and acceptable responses is done using the genetic algorithm. During the experiments and the comparison we made, we found that the proposed algorithm yielded a Path of 28. 48 if the lengths of the Paths obtained in an environment similar to the other algorithm of 15 / 32, 29. 5 and 29. 49, which is more than the proposed method.

The primary purpose of each autonomous exit parking system is to facilitate the process of exiting the vehicle, emphasizing the comfort and safety of driving in the absence of almost any human effort. In this paper, the problem of exit parking for autonomous vehicles is addressed. A nonlinear kinematic model is presented based on the geometric relationship of the vehicle velocities, and a linear time-varying discrete-time model of the vehicle is obtained for utilizing the optimal control strategy. The proposed Path planning algorithm is based on the minimization of a geometric cost function. This algorithm works for ample space exit parking in Single-Maneuver and tight spaces in Multi-Maneuver exit parking. Finally, an optimal discrete-time linear quadratic control approach is hired to minimize a quadratic cost function. To evaluate the performance of the proposed algorithm, the control system is simulated by MATLAB/Simulink software. The results show that the optimal control strategy is well able to design and follow the desired Path in each of the exit parking maneuvers.

This paper applies the Q-learning method, a reinforcement learning (RL) technique, to a quadrotor for finding a low-noise trajectory while avoiding obstacles. The proposed method introduces a novel Q-value function, resulting in obtaining a 2D surface that preserves the features of the environment. Therefore, the Path-finding problem in a 3D space is simplified into a 2D space problem. Since the data is obtained based on the pre-calculated 2D surface, online Path planning in the presence of unpredictable environmental changes is handled with markedly reduced computational complexities, effectively resolving a significant challenge in this area. The Q-learning algorithm is developed by defining two cost functions to avoid obstacles and reduce the observers' perceived noise level. To find the noise Sound Pressure Level (SPL), the perceived noise model is derived through the Gutin equation. In addition, the Octomap 3D optimizer is used to map the obstacles. Compared to the related works, noise observers are used vertically and horizontally, leading to more accurate environmental details. Moreover, the proposed algorithm leads to global optimal Paths and avoids local minimum points commonly produced by similar optimization approaches. Finally, the performance of the proposed methodology in Path finding and noise reduction is further demonstrated via a practical example of a quadrotor.