A real-time path planning approach without the computation of Cspace obstacles

Abstract

An important concept proposed in the early stage of robot path planning field is the shrinking of the robot to a point and meanwhile expanding of the obstacles in the workspace as a set of new obstacles. The resulting grown obstacles are called the Configuration Space (Cspace) obstacles. The find-path problem is then transformed into that of finding a collision free path for a point robot among the Cspace obstacles. However, the research experiences obtained so far have shown that the calculation of the Cspace obstacles is very hard work when the following situations occur: 1. both the robot and obstacles are not polygons and 2. the robot is allowed to rotate. This situation is even worse when the robot and obstacles are three dimensional (3D) objects with various shapes. Obviously a direct path planning approach without the calculation of the Cspace obstacles is strongly needed. This paper presents such a new real-time robot path planning approach which, to the best of our knowledge, is the first one in the robotic community. The fundamental ideas are the utilization of inequality and optimization technique. Simulation results have been presented to show its merits.

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

“…Path planning is an essential part of mobile robotic systems to find the optimal path [1][2][3][4]. The boundary values of velocity, acceleration, and deceleration of a mobile robot (MR) are defined as closed mathematical forms over the spatial path.…”

“…The proposed algorithms are not time-consuming if the path is planned as smooth curves. A real-time robot path planning approach based on the utilization of inequality and optimization technique has been presented in [3]. The problem of finding a path has been transformed into finding a collision free path without calculating the configuration space obstacles.…”

Adaptive boundary-following algorithm guided by artificial potential field for robot navigation

“…The C-space provides a powerful abstraction that converts the complicated models into the general problem of computing a path that traverses a manifold [16]. However, research experience obtained so far has shown that the calculation of the C-space obstacles is hard when the following situations occur: 1) Both the robot and obstacles are not polygons; and 2) The robot is allowed to rotate [26]. For instance, Kavraki [27] proposed a new method to compute the C-space using fast Fourier transformation.…”

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

“…These inputs determine the repulsive force that must be applied to the robot in order to repulse the robot away from a moving or stationary obstacle. The proposed model Vislbllity graph (Filliatet al 2003;Meyer and Filliat 2003;Jing 2005;Wang et al 2004;Baran Hui and Kumar Pratihar 2009;Masoud and Masoud et al 2000;Hart and Timmis 2008;Jianping and Yang 2003;Jaradat et al 2011;Koren and Borenstein 1991) 4 4 -Probabilistic road map Meyer and Filliat 2003;Jing 2005;Wang et al 2004;Baran Hui and Kumar Pratihar 2009;Masoud and Masoud et al 2000;Hart and Timmis 2008;Jianping and Yang 2003;Jaradat et al 2011;Koren and Borenstein 1991) 4 --Path planning and velocity planning Meyer and Filliat 2003;Jing 2005;Wang et al 2004;Baran Hui and Kumar Pratihar 2009;Masoud and Masoud et al 2000;Hart and Timmis 2008;Jianping and Yang 2003;Jaradat et al 2011;Koren and Borenstein 1991) 4 4 -Artificial potential fields Meyer and Filliat 2003;Jing 2005;Wang et al 2004;Baran Hui and Kumar Pratihar 2009;Masoud and Masoud et al 2000;Hart and Timmis 2008;Jianping and Yang 2003;Jaradat et al 2011;Koren and Borenstein 1991) Genetic algorithms (Hart and Timmis 2008;…”