An online path planning approach of mobile robot based on particle filter

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -Path planning in unknown or partly unknown environment is a quite complex task, partly because it is an evolving globally optimal path affected by the motion of the robot and the changing of environmental information. The purpose of this paper is to propose an online path planning approach for a mobile robot, which aims to provide a better adaptability to the motion of the robot and the changing of environmental information. Design/methodology/approach -This approach treats the globally optimal path as a changing state and estimates it online with two steps: prediction step, which predicts the globally optimal path based on the motion of the robot; and updating step, which uses the up-to-date environmental information to refine the prediction. Findings -Simulations and experiments show that this approach needs less time to reach the destination than some classical algorithms, provides speedy convergence and can adapt to unexpected obstacles or very limited prior environmental information. The better performances of this approach have been proved in both field and indoor environments. Originality/value -Compared with previous works, the paper's approach has three main contributions. First, it can reduce the time consumed in reaching the destination by adopting an online path planning strategy. Second, it can be applied in such environments as those with unexpected obstacles or with only limited prior environmental information. Third, both motion error of the robot and the changing of environmental information are considered, so that the global adaptability to them is improved.

A New Path Evaluation Method for Path Planning With Localizability

“…Zhang H et al have recently proposed an enhanced RRT (Rapidly-Exploring Random Tree) algorithm, which has incorporated a regression mechanism for better efficiency. Gao et al developed an online global path planning algorithm for dynamic environments, which models the global optimal path as a dynamically changing state and employs Particle Filters to track it [3]. Intelligent algorithms based global path planning methods, such as Neural Network based group, Ant Colony algorithm based group, Particle Swarm Optimization algorithm and Genetic Algorithm based groups also attracted great attention due to their superior performance.…”

“…Ferguson splines are employed to describe the path for the robot due to their smoothness and the convenience in path following as it has been done in our prior research [3]. So path R is described as n smoothly connected Ferguson splines r i (i = 1 .…”

“…For a path R, an evaluation function E(R, MC) is created in [3], which represents the traditional requirements for paths that are shorter and further away from the obstacle. To reduce the impact of the localizability, the E(R, MC) is enlarged to a new path evaluation function E p (R, MC) as shown in formula (19).…”

“…As one of the fundamental functions of mobile robots, path planning provides the capability to find an optimal path in complex environments from a given start point to the destination, so that many kinds of missions can be carried out. For decades, path planning has attracted plenty of attention with most existing research focused on finding the optimal path according to various criteria including the shortest length, least time [1] or least energy consumption [2], path smoothness, obstacle-free path, ease of implementation, better adaptability to dynamic environments [3], [4], smallest amount of environmental information requests, etc. However, to the best of our knowledge, the localizability of a path has been less of a consideration.…”

Research on mobile robot navigation based on Kinect depth information

“…System will find the target point after continuously cycles at last. This approach deletes some unnecessary searching path that makes it high efficiency [6] .…”