This paper aims at presenting a new two-stage algorithm to find or approaching global minimum for manipulator in optimization problems. It uses a combination of mathematical optimization method and an evolutionary approach. The mathematical method is based on the indirect solution of open loop OPTIMAL control problem and the evolutionary method is based on Genetic Algorithm (GA). Some initial guesses generated by GA to produce OPTIMAL solution by OPTIMAL control are used. Then, the cost function is calculated for every OPTIMAL solution and the best solutions are chosen for the next step. In the next step of the algorithm, these solutions are used to produce the new initial guesses. OPTIMAL control problem is then solved for each guess again, and its cost is calculated. This process continues until the minimum cost value is achieved. In order to improve performance of the algorithm, a new GA operator is introduced in addition to the conventional GA ones to select the pair chromosomes for crossover. The proposed method eliminates the problem of OPTIMAL control which is trapped in local OPTIMAL point and tries to obtain the global minimum. The effectiveness of the method is shown by simulation.

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.

In this paper, dynamic modeling, OPTIMAL PATH PLANNING and control scheme on a redundant parallel cable robotis presented. PATH PLANNING in parallel robots necessitates the consideration of robot’s kinematics to discern the singularities in the workspace. Also, dynamics analysis is required to consider actuation constraints. To this end, kinematics and dynamics of cable driven redundant parallel robot is derived. In this modeling, cables are assumed to be rigid with negligible mass and hence, tension and sagging along the cable are neglected. Next, a sampling-based algorithm upon rapidly-exploring random tree is developed to increase the convergence rate. In this scheme, distance, epochs and safety are considered as optimization constraints. To evaluate the performance of the proposed algorithm in collision avoidance, a number of obstacles have been considered too. Tracking of the planned PATH has been handled using a feed-forward controller in the presence of obstacles. Regarding the redundancy feature of robot, a redundancy resolution scheme is considered for OPTIMAL force distribution. PATH PLANNING and control algorithms are implemented on the RoboCab (ARAS Lab.) and experimental results reveal the efficiency of the proposed schemes.

This paper proposes a method based on the reinforcement Q-learning to solve the problem of fully autonomous OPTIMAL PATH PLANNING of a space robot by increasing the number of available satellites in earth orbits, designation, and implementation of satellite orbital servicing stations considered by researchers. Nowadays, by considering the advancement in robotics science, space robots could be chosen as a part of solution for maintaining the damaged satellites in earth’s orbits. Guidance, control, and navigation of space robots throughout docking and joint maneuvers need a high degree of precision. In this paper, reinforcement Q-learning algorithm functionality in PATH PLANNING is analyzed through various computational simulations. The finding results from computational simulations demonstrate the usefulness of the mentioned approach.

In this paper, an optimization problem for the observer trajectory in the bearings-only surface moving target tracking (BOT) is studied. The BOT depends directly on the observability of the target's position in the target/observer geometry or the OPTIMAL observer maneuver. Therefore, the maximum lower band of the Fisher information matrix is opted as an independent criterion of the target estimator. First, modeling of the OPTIMAL control problem of the observer PATH is presented based on the orthogonal Chebyshev polynomial. Then, a control law for the observer direction, which is independent of the initial conditions, is obtained using the direct numerical optimization. The advantages of the proposed model include maximization of the total maneuver time, calculation of the control law at the start time of the maneuver, and high flexibility in applying the tracking constraints of the observer's motion. The efficiency of the proposed algorithm is compared with the conventional PATH optimization methods using the Monte Carlo. In addition, the performance of the algorithm is evaluated in different scenarios for target tracking, including remote, near, moving, and stationary, and its reliability is investigated. It is also applied in the surface submarine tracking problem using sonar.

The development of motor vehicle use has reduced irreversible fossil fuels, environmental pollution, and reduced physical mobility, despite significant reductions in travel times, especially over long distances. On the other hand, this saving is not achieved during peak hours, especially at short travel distances. One way to solve this problem, especially in metropolises that face more environmental challenges, is to develop intercity transportation for short distances by bicycle. The purpose of this research is to select the OPTIMAL safe bicycle route in District 10 of Mashhad Metropolitan Municipality due to the modernity of the area, high traffic load, and at the same time having the capacity to develop bicycle-based transportation. The research method in this paper is descriptive-analytical and based on Analytic Hierarchy Process (AHP). The method is to collect documentary and field information. Multi-criteria analysis of indicators in this research has been done by expert choice software in digital mapping layers and simulated in GIS environment. The input of this model will be the information and documents examined by the target environment and its output will be maps that show the potential of the area's roads to be used as a safe bicycle route. The findings of this study showed that the safety index of intersections and the type of passages with 27% had the highest impact factor and the index of distance to bus stations with an impact factor of 2% had the lowest coefficient of importance. Therefore, it can be concluded that Shahid Rasti Street and Andisheh Boulevard, after Shahed and Shahed Boulevards intersections, have the greatest potential for safe cycling, respectively.