The inertial forces and moments, due to the motion of robotic arms installed on a mobile base, lead to reaction forces on the moving base which may cause its unexpected motion. In this article, a method of designing a PATH of motion in the Cartesian space between the initial and final positions is presented which guarantees no reaction on the moving base. To this end, developing the system dynamics model, the moment equations are derived. Based on the conservation of momentum in the absence of any external force and moments, the angular motion due to the motion of robotic arms is solved. Then, based on the definition of reaction null-space map for dynamic coupling matrix, the joint speeds are projected to the reaction null-space, to obtain the joint speeds in this space. Next, using numerical integration of the obtained joint rates, the motion in the joint space with no reaction on the base is obtained. Therefore, motion of robotic arms according to these joint specifications, the total momentum of the system remains zero, and due to no reaction forces applied on the moving base, its position and attitude remains unchanged.

PATH PLANNING of mobile robot is one of the most important topics in mobile robotic discussion. The aim of this study is to find a continuous PATH from an initial position to the final target; So that, it should be free of collision and optimal or near to optimal. Since PATH PLANNING problem of robot is one type of optimization problems, the evolutionary algorithms can be used to solve this problem. Nowadays, clonal selection algorithm is frequently used to solve the problems because of having valuable computational characteristics. But very little attempts have been done in the field of using this method to solve robot PATH PLANNING problem. Few accomplished attempts are actually a kind of improved genetic algorithm. In this research, an efficient method for robot PATH PLANNING in the presence of obstacles is designed using all the features of the clonal selection algorithm. The proposed method is evaluated in various environments with different runs in terms of the proposed PATH length criteria and the number of generations needed to generate the PATH. Based on the results of experiments, the proposed method shows better performance than the genetic algorithm in all environments and all the evaluation parameters. Especially, by increasing the number of obstacles vertices and also concave obstacles, the proposed method shows much more efficient performance than the genetic algorithm. Also, comparing the performance of the proposed method with the BPSO algorithm (presented in another study) indicates the superiority of PATH PLANNING algorithm based on the clonal selection.

Numerous methods have been developed to solve the motion PLANNING problem, such as Potential Fields and Voronoi Diagrams. In order to benefit from the superiorities of these two methods and compensate their weaknesses, we propose an integrated model for PATH PLANNING. In this paper, after building the environment's Generalized Voronoi diagram, we introduce the Pruned Generalized Voronoi Diagram. Then a potential function is assigned to it and incorporated into the Potential Field model. Finally, a steepest descent, mildest ascent bidirectional search technique is used for PATH PLANNING. This enables us to overcome the famous local minima problem in Potential Fields. The algorithm performs satisfactorily in complicated problems, especially mazes.

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.

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.