In this article, the Modified Dynamic Path-Planning Algorithm is designed. The problem of overlapped obstacles (e. g., forbidden flight zones) is solved while the pervious Dynamic Path Planning methods could not response to this problem. This Modified introduced Algorithm can be applied in the complex environment and complicated applications without any limitation on the states of obstacles and targets while the pervious Dynamic Path Planning Algorithm stops working when UAVs is inside the target circle. In this article, by considering a virtual obstacle, formation flying Path Planning is introduced. Numerical simulations are performed to verify the feasibility and ability of this proposed Algorithm. This newly proposed Algorithm performed for coordinated and group target tracking of multiple UAVs.

In this research, the aim is to find the shortest Path, without encountering the existing obstacles, to guide a missile in a linear way through fixed and moving obstacles towards a moving target. The movements of the obstacles and the target are unknown in the first place, but after discovering and finding the target and obstacles, the method proposed in this article can generate commands for the missile to track the target until it is reached. The proposed Algorithm of this article is designed in such a way that it can be ensured that the missile finds the shortest possible Path and approaches the target by avoiding the obstacles completely. On the other hand, considering that the presented Algorithm does not use all the information available in the environment and in order to control the volume of calculations in the next cycles, corrective operations are used in the Algorithm, so it is expected that the proposed Algorithm will reach the optimal solution in a very short time. Also, we have simulated some complex scenarios to test the Algorithm in MATLAB software, the results of which show the convergence of the Algorithm in finding the optimal Path leading to the moving or stationary target while avoiding moving or stationary obstacles in the appropriate time frame.

​One of the most important issues in the field of the mobile swarm robot is the problem of Path Planning.  The goal of Path Planning is determining the optimal trajectory of the Path for multi-robot in a defined search space that directs the robot from a start point to a destination point. This optimal trajectory should be such that the robot does not encounter obstacles and other robots. It should be mentioned that the optimal trajectory should also meets the requirements of being safe and optimal; moreover the optimal trajectory should be the lowest cost Path. So far, numerous literature have been devoted to swarm robotic Path Planning in two dimension environments. But in the real environment, the Path is not quite flat, and the search space has lots of roughness. In this paper, the problem of Path Planning for a swarm of robots will be solved in a three-dimension (3D) feasible search space using the improved particle swarm optimization method. In the experiments, the proposed method is examined in term of number of robots and different environments. Simulation results demonstrate the effectiveness of the proposed approach in minimizing Path length and travel time.

KinoDynamic Path Planning is an open challenge in unmanned autonomous vehicles and is considered an NP-Hard problem. Planning a feasible Path for vertical take-off and landing quadrotor (VTOL-Q) from an initial state to a target state in 3D space by considering the environmental constraints such as moving obstacles avoidance and non-holonomic constraints such as hard bounds of VTOL-Q is the key motivation of this study. To this end, let us propose the any-time randomized kinoDynamic (ATRK) Path-Planning Algorithm applicable in the VTOL-Q. ATRK Path-Planning Algorithm is based on the Rapidly-exploring random trees (RRT) and consists of three main components: high-level, mid-level, and low-level controller. The high-level controller utilizes a randomized sampling-based approach to generate offspring vertices for rapid exploring and expanding in the configuration space. The mid-level controller uses the any-time method to avoid collision with moving obstacles. The low-level controller with a six-DOF Dynamic model accounts for the kinoDynamic constraints of VTOL-Q in the randomized offspring vertices to plan a feasible Path. Simulation results on three different test-scenario demonstrate the kinoDynamic constraints of the VTOL-Q are integrated into the randomized offspring vertices. Also, in presence of moving obstacles, the ATRK re-plans the Path in the local area as through an any-time approach.

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.

A fuzzy logic based bi-objective Path Planning Algorithm for multiple mobile robots in unknown Dynamic environment.

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.

In this paper we introduce an improvement in the Path Planning Algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the Algorithm is to find a Path through other playing robots to the ball, which should be as short as possible and also safe enough. Ferguson splines create preliminary Paths using random generated parameters.The random parameters are then iteratively fed into the PSO for optimization and converging to optimal Path. Our proposed method makes a balance between the Path shortness and the safety which makes it more efficient for humanoid soccer playing robots and also for any other crowded environment with various moving obstacles. Experimental results show that our proposed Algorithm converges in at most 60 iterations with the average accuracy of 92% and the maximum Path length overhead of 14% for Planning the shortest and yet safest Path.

This paper presents a Transimpedance Amplifier (TIA) for high sensitivity Near Infrared Spectroscopy (NIRS). The proposed TIA is based on the Regulated Cascode (RGC) structure with an extra transistor employed to implement additional feed-forward Path and achieve higher gain values. The extra transistor senses a partially amplified input signal, available in the conventional circuit, and conveys an additional ac current into the load, which provides a higher gain. In addition, a bandwidth extension method is introduced using a capacitor and resistor, which can improve amplifier’s bandwidth by 40%. The proposed TIA is designed in 0.18µm CMOS technology and achieves a transimpedance gain of 101.9dB with a -3dB bandwidth of 91.2 MHz considering 2pF of photodiode capacitance at the TIA input. The input referred noise is 4.4pA/√Hz while dissipating 151µW power.

In this paper, Generation Expansion Planning (GEP), is modeled as an optimization problem in which the objective function is to minimize the total investment, operation, and outage (energy not served) costs of power system as well as salvage value of investment costs. Generation system reliability is assessed and provided by means of EENS and LOLP indices. To solve the GEP problem, a new Modified Shuffled Frog Leaping namely MSFL Algorithm is proposed. A new frog leaping rule and a new strategy for frog distribution into memeplexes is introduced to improve the local exploration and performance of the original SFL Algorithm. To show the effectiveness of the MSFL Algorithm, it is applied to a test system with 15 existing power plants and 5 types of new candidates, for a 12-years and a 24-years Planning horizon. The original SFL Algorithm and the Genetic Algorithm (GA) are also applied to solve the GEP problem. Simulation results show the advantages of the proposed MSFL Algorithm over the original SFL and GA.