Graphical path method

Abstract: The objective of the path planning problem for a mobile robot is to generate a collision-free path from a starting position to a target position with respect to a certain fitness function, such as distance. Although, over the last few decades, path planning has been studied using a number of methodologies, the complicated and dynamic environment increases the complexity of the problem and makes it difficult to find an optimal path in reasonable time. Another issue is the existence of uncertainty in previous approaches. In this paper, we propose a new methodology to solve the path planning problem in two steps. First, the surrounding point set (SPS) is determined where the obstacles are circumscribed by these points. After the initial feasible path is generated based on the SPS, we apply a path improvement algorithm depending upon the former and latter points (PI_FLP), in which each point in the path is repositioned according to two points on either side. Through the SPS, we are able to identify the necessary points for solving path planning problems. PI_FLP can reduce the overall distance of the path, as well as achieve path smoothness. The SPS and PI_FLP algorithms were tested on several maps with obstacles and then compared with other path planning methods As a result, collision-free paths were efficiently and consistently generated, even for maps with narrow geometry and high complexity.

Abstract: A system for determining a path in a graphical diagram includes a processor coupled to an input device, an output device, a memory, and a data retrieval device. The processor executes an application to determine a path through a portion of a graphical diagram, to record path information defining the path, and to reproduce a copy of the path using the path information.

Abstract: Flight path planning of UAV is a complicated optimum problem. The research in this field is usually classified as two directions: optimal path planning without considering the computation cost and real-time suboptimal path planning. Aimed at the two problems, this paper presents two methods of path planning of UAV. One method is based on heuristically search. In this method, we search the threat net by using A-star algorithm. A shortest suboptimum path is obtained, which is composed of the lines on the Voronoi diagram, and then considering the UAV's turn constraint, the path can be smoothed by geometry method. The other method is based on genetic algorithm and potential fields technology. Because potential fields, however popular, have shortcomings, viz, trap situations due to local minima, the genetic algorithm is introduced. In this method, Firstly, construct the threats' Delaunay triangle net based on the principle of nearest neighborhood and a safe planning path goes across the lines of the triangles. Then designate each point (position of a threat) on the left of the path a index 0 and on the right 1 and only designate the points of line which is not passed the same symbol. Thus, based on the directions of passing the lines of Delaunay triangle, a kind of encoding is designed on the principle of "Left 0, Right 1". At last, a global planning path can obtained by the genetic algorithm and potential fields technology. At the same time, simulation results of the two path planning methods are given.

Abstract: Mobile robots work in different kinds of environment, and it is necessary for them to move and maneuver in places with objects and obstacles. In order to navigate the robot in a collision free path, path planning algorithms have been presented. The main goal of path planning is to determine the optimal possible path between the initial point and the defined goal position in the minimal possible time. In this work, a path planning method by utilizing genetic algorithm (GA) is presented. The optimized path in terms of length and cost is generated by GA optimization. The proposed method is a global path planning method with hexagonal grid map modelling. It reads the map of the environment and plans the optimized path by using GA method simulated in MATLAB R2012b software. The simulation results are presented and analyzed.